Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

Complex quantized minimum error entropy with fiducial points: theory and application in model regression

Minimum error entropy with fiducial points (MEEF) has gained significant attention due to its excellent performance in mitigating the adverse effects of non-Gaussian noise in the fields of machine learning and signal processing. However, the original MEEF algorithm suffers from high computational complexity due to the double summation of error samples. The quantized MEEF (QMEEF), proposed by Zheng et al. alleviates this computational burden through strategic quantization techniques, providing a more efficient solution. In this paper, we extend the application of these techniques to the complex domain, introducing complex QMEEF (CQMEEF). We theoretically introduce and prove the fundamental properties and convergence of CQMEEF. Furthermore, we apply this novel method to the training of a range of Linear-in-parameters (LIP) models, demonstrating its broad applicability. Experimental results show that CQMEEF achieves high precision in regression tasks involving various noise-corrupted datasets, exhibiting effectiveness under unfavorable conditions, and surpassing existing methods across critical performance metrics. Consequently, CQMEEF not only offers an efficient computational alternative but also opens up new avenues for dealing with complex data in regression tasks.

Brain age prediction based on brain region volume modeling under broad network field of view

BACKGROUND AND OBJECTIVE

Brain region volume from Structural Magnetic Resonance Imaging (sMRI) can directly reflect abnormal states in brain aging. While promising for clinical brain health assessment, existing volume-based brain age prediction methods fail to explore both linear and nonlinear relationships, resulting in weak representation and suboptimal estimates.

METHODS

This paper proposes a brain age prediction method, RFBLSO, based on Random Forest (RF), Broad Learning System (BLS), and Leave-One-Out Cross Validation (LOO). Firstly, RF is used to eliminate redundant brain regions with low correlation to the target value. The objective function is constructed by integrating feature nodes, enhancement nodes, and optimal regularization parameters. Subsequently, the pseudo-inverse method is employed to solve for the output coefficients, which facilitates a more accurate representation of the linear and nonlinear relationships between volume features and brain age.

RESULTS

Across various datasets, RFBLSO demonstrates the capability to formulate brain age prediction models, achieving a Mean Absolute Error (MAE) of 4.60 years within the Healthy Group and 4.98 years within the Chinese2020 dataset. In the Clinical Group, RFBLSO achieves measurement and effective differentiation among Healthy Controls (HC), Mild Cognitive Impairment (MCI), and Alzheimer's disease (AD) (MAE for HC, MCI, and AD: 4.46 years, 8.77 years, 13.67 years; the effect size η2 of the analysis of variance for AD/MCI vs. HC is 0.23; the effect sizes of post-hoc tests are Cohen's d = 0.74 (AD vs. MCI), 1.50 (AD vs. HC), 0.77 (MCI vs. HC)). Compared to other linear or nonlinear brain age prediction methods, RFBLSO offers more accurate measurements and effectively distinguishes between Clinical Groups. This is because RFBLSO can simultaneously explore both linear and nonlinear relationships between brain region volume and brain age.

CONCLUSION

The proposed RFBLSO effectively represents both linear and nonlinear relationships between brain region volume and brain age, allowing for more accurate individual brain age estimation. This provides a feasible method for predicting the risk of neurodegenerative diseases.

Broad Multitask Learning System With Group Sparse Regularization

The broad learning system (BLS) featuring lightweight, incremental extension, and strong generalization capabilities has been successful in its applications. Despite these advantages, BLS struggles in multitask learning (MTL) scenarios with its limited ability to simultaneously unravel multiple complex tasks where existing BLS models cannot adequately capture and leverage essential information across tasks, decreasing their effectiveness and efficacy in MTL scenarios. To address these limitations, we proposed an innovative MTL framework explicitly designed for BLS, named group sparse regularization for broad multitask learning system using related task-wise (BMtLS-RG). This framework combines a task-related BLS learning mechanism with a group sparse optimization strategy, significantly boosting BLS's ability to generalize in MTL environments. The task-related learning component harnesses task correlations to enable shared learning and optimize parameters efficiently. Meanwhile, the group sparse optimization approach helps minimize the effects of irrelevant or noisy data, thus enhancing the robustness and stability of BLS in navigating complex learning scenarios. To address the varied requirements of MTL challenges, we presented two additional variants of BMtLS-RG: BMtLS-RG with sharing parameters of feature mapped nodes (BMtLS-RGf), which integrates a shared feature mapping layer, and BMtLS-RGf and enhanced nodes (BMtLS-RGfe), which further includes an enhanced node layer atop the shared feature mapping structure. These adaptations provide customized solutions tailored to the diverse landscape of MTL problems. We compared BMtLS-RG with state-of-the-art (SOTA) MTL and BLS algorithms through comprehensive experimental evaluation across multiple practical MTL and UCI datasets. BMtLS-RG outperformed SOTA methods in 97.81% of classification tasks and achieved optimal performance in 96.00% of regression tasks, demonstrating its superior accuracy and robustness. Furthermore, BMtLS-RG exhibited satisfactory training efficiency, outperforming existing MTL algorithms by 8.04-42.85 times.

Unsupervised Feature Selection for High-Order Embedding Learning and Sparse Learning

The majority of the unsupervised feature selection methods usually explore the first-order similarity of the data while ignoring the high-order similarity of the instances, which makes it easy to construct a suboptimal similarity graph. Furthermore, such methods, often are not suitable for performing feature selection due to their high complexity, especially when the dimensionality of the data is high. To address the above issues, a novel method, termed as unsupervised feature selection for high-order embedding learning and sparse learning (UFSHS), is proposed to select useful features. More concretely, UFSHS first takes advantage of the high-order similarity of the original input to construct an optimal similarity graph that accurately reveals the essential geometric structure of high-dimensional data. Furthermore, it constructs a unified framework, integrating high-order embedding learning and sparse learning, to learn an appropriate projection matrix with row sparsity, which helps to select an optimal subset of features. Moreover, we design a novel alternative optimization method that provides different optimization strategies according to the relationship between the number of instances and the dimensionality, respectively, which significantly reduces the computational complexity of the model. Even more amazingly, the proposed optimization strategy is shown to be applicable to ridge regression, broad learning systems and fuzzy systems. Extensive experiments are conducted on nine public datasets to illustrate the superiority and efficiency of our UFSHS.

Motion control strategy for robotic arm using deep cascaded feature-enhancement Bayesian broad learning system with motion constraints

Intelligent control strategies can significantly enhance the efficiency of model parameter adjustment. However, existing intelligent motion control strategies for robotic arms based on the broad learning system lack sufficient accuracy and fail to account for the effects of joint motion limitations on overall control performance. To address the aforementioned challenges, this paper proposes a robotic arm motion control strategy based on a deep cascaded feature-enhanced Bayesian broad learning system with motion constraints (MC-DCBLS). Firstly, the motion control strategy based on a deep cascaded feature-enhanced Bayesian broad learning system (DCBBLS) is designed, which simplifies the modeling process and significantly improves control accuracy. Secondly, the motion constraint mechanism is introduced to optimize the control strategy to ensure that the robotic arm motion does not break through the physical limit. Finally, the parameter constraints of the control strategy network were obtained by introducing the Lyapunov theory to ensure the stability of the robotic arm motion control. The effectiveness of the proposed control strategy was validated through both simulations and physical experiments. The results demonstrated that the strategy significantly improved the accuracy of robotic arm motion control, with the root mean square error (RMSE) in position tracking reduced to 0.038 rad. This represents a 61.26% reduction in error compared to existing techniques.

Adaptive Memory Broad Learning System for Unsupervised Time Series Anomaly Detection

Time series anomaly detection is the process of identifying anomalies within time series data. The primary challenge of this task lies in the necessity for the model to comprehend the characteristics of time-independent and abnormal data patterns. In this study, a novel algorithm called adaptive memory broad learning system (AdaMemBLS) is proposed for time series anomaly detection. This algorithm leverages the rapid inference capabilities of the broad learning algorithm and the memory bank's capacity to differentiate between normal and abnormal data. Furthermore, an incremental algorithm based on multiple data augmentation techniques is introduced and applied to multiple ensemble learners, thereby enhancing the model's effectiveness in learning the characteristics of time series data. To bolster the model's anomaly detection capabilities, a more diverse ensemble approach and a discriminative anomaly score are recommended. Extensive experiments conducted on various real-world datasets demonstrate that the proposed method exhibits superior inference speed and more accurate anomaly detection compared to the existing competitors. A detailed experimental investigation is presented to elucidate the effectiveness of the proposed method and the underlying reasons for its efficacy.

Broad learning system based on fractional order optimization

Due to its efficient incremental learning performance, the broad learning system (BLS) has received widespread attention in the field of machine learning. Scholars have found in algorithm research that using the maximum correntropy criterion (MCC) can further improves the performance of broad learning in handling outliers. Recent studies have shown that differential equations can be used to represent the forward propagation of deep learning. The BLS based on MCC uses differentiation to optimize parameters, which indicates that differential methods can also be used for BLS optimization. But general methods use integer order differential equations, ignoring system information between integer orders. Due to the long-term memory property of fractional differential equations, this paper innovatively introduces fractional order optimization into the BLS, called FOBLS, to better enhance the data processing capability of the BLS. Firstly, a BLS is constructed using fractional order, incorporating long-term memory characteristics into the weight optimization process. In addition, constructing a dynamic incremental learning system based on fractional order further enhances the ability of network optimization. The experimental results demonstrate the excellent performance of the method proposed in this paper.

An Incremental-Self-Training-Guided Semi-Supervised Broad Learning System

The broad learning system (BLS) has recently been applied in numerous fields. However, it is mainly a supervised learning system and thus not suitable for specific practical applications with a mixture of labeled and unlabeled data. Despite a manifold regularization-based semi-supervised BLS, its performance still requires improvement, because its assumption is not always applicable. Therefore, this article proposes an incremental-self-training-guided semi-supervised BLS (ISTSS-BLS). Distinctive to traditional self-training, where all unlabeled data are labeled simultaneously, incremental self-training (IST) obtains unlabeled data incrementally from an established sorted list based on the distance between the data and their cluster center. During iterative learning, a small portion of labeled data is first used to train BLS. The system recursively self-updates its structure and meta-parameters using: 1) the double-restricted mechanism and 2) the dynamic neuron-incremental mechanism. The double-restricted mechanism is beneficial to preventing the introduction of incorrect pseudo-labeled samples, and the dynamic neuron-incremental mechanism guides the self-updating of the network structure effectively based on the training accuracy of the labeled data. These strategies guarantee a parsimonious model during the update. Besides, a novel metric, the accuracy-time ratio (A/T), is proposed to evaluate the model's performance comprehensively regarding time and accuracy. In experimental verifications, ISTSS-BLS performs outstandingly on 11 datasets. Specifically, the IST is compared with the traditional one on three scales data, saving up to 52.02% learning time. In addition, ISTSS-BLS is compared with different state-of-the-art alternatives, and all results indicate that it possesses significant advantages in performance.

Bearing Rigidity-Based Flocking Control of AUVs via Semi-Supervised Incremental Broad Learning

Flocking control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine coordination missions. However, there is still a research gap on the flocking of AUVs in weak communication and complex marine environment. This article attempts to fill up the above research gap from graph theory and intelligent learning perspectives. We first employ the bearing rigidity graph to describe the topology relationships of AUVs, through which an iterative gradient decent-based localization estimator is provided to obtain the position information. In order to improve the localization accuracy and energy efficiency, a min-weighted bearing rigidity graph generation strategy is developed. Along with this, we adopt the semi-supervised broad learning system (BLS) to design the model-free flocking controllers for AUVs in obstacle environment. The innovations of this article are summarized as follows: 1) the min-weighted bearing rigidity-based localization strategy can balance the localization accuracy and communication consumption as compared to the neighboring rule-based solutions and 2) the semi-supervised broad learning-based flocking controller can decrease the training time and solve the label limit over the supervised learning-based controllers. Finally, simulation and experimental studies are provided to verify the effectiveness.

Co-Training Broad Siamese-Like Network for Coupled-View Semi-Supervised Learning

Multiview semi-supervised learning is a popular research area in which people utilize cross-view knowledge to overcome the limitation of labeled data in semi-supervised learning. Existing methods mainly utilize deep neural network, which is relatively time-consuming due to the complex network structure and back propagation iterations. In this article, co-training broad Siamese-like network (Co-BSLN) is proposed for coupled-view semi-supervised classification. Co-BSLN learns knowledge from two-view data and can be used for multiview data with the help of feature concatenation. Different from existing deep learning methods, Co-BSLN utilizes a simple shallow network based on broad learning system (BLS) to simplify the network structure and reduce training time. It replaces back propagation iterations with a direct pseudo inverse calculation to further reduce time consumption. In Co-BSLN, different views of the same instance are considered as positive pairs due to cross-view consistency. Predictions of views in positive pairs are used to guide the training of each other through a direct logit vector mapping. Such a design is fast and effectively utilizes cross-view consistency to improve the accuracy of semi-supervised learning. Evaluation results demonstrate that Co-BSLN is able to improve accuracy and reduce training time on popular datasets.

Robust Incremental Broad Learning System for Data Streams of Uncertain Scale

Due to its marvelous performance and remarkable scalability, a broad learning system (BLS) has aroused a wide range of attention. However, its incremental learning suffers from low accuracy and long training time, especially when dealing with unstable data streams, making it difficult to apply in real-world scenarios. To overcome these issues and enrich its relevant research, a robust incremental BLS (RI-BLS) is proposed. In this method, the proposed weight update strategy introduces two memory matrices to store the learned information, thus the computational procedure of ridge regression is decomposed, resulting in precomputed ridge regression. During incremental learning, RI-BLS updates two memory matrices and renews weights via precomputed ridge regression efficiently. In addition, this update strategy is theoretically analyzed in error, time complexity, and space complexity compared with existing incremental BLSs. Different from Greville's method used in the original incremental BLS, its results are closer to the solution of one-shot calculation. Compared with the existing incremental BLSs, the proposed method exhibits more stable time complexity and superior space complexity. The experiments prove that RI-BLS outperforms other incremental BLSs when handling both stable and unstable data streams. Furthermore, experiments demonstrate that the proposed weight update strategy applies to other random neural networks as well.

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification

Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.

Enhanced Broad-Learning-Based Dangerous Driving Action Recognition on Skeletal Data for Driver Monitoring Systems

Recognizing dangerous driving actions is critical for improving road safety in modern transportation systems. Traditional Driver Monitoring Systems (DMSs) often face challenges in terms of lightweight design, real-time performance, and robustness, especially when deployed on resource-constrained embedded devices. This paper proposes a novel method based on 3D skeletal data, combining Graph Spatio-Temporal Feature Representation (GSFR) with a Broad Learning System (BLS) to overcome these challenges. The GSFR method dynamically selects the most relevant keypoints from 3D skeletal data, improving robustness and reducing computational complexity by focusing on essential driver movements. The BLS model, optimized with sparse feature selection and Principal Component Analysis (PCA), ensures efficient processing and real-time performance. Additionally, a dual smoothing strategy, consisting of sliding window smoothing and an Exponential Moving Average (EMA), stabilizes predictions and reduces sensitivity to noise. Extensive experiments on multiple public datasets demonstrate that the GSFR-BLS model outperforms existing methods in terms of accuracy, efficiency, and robustness, making it a suitable candidate for practical deployment in embedded DMS applications.

Dynamic Neural Network Structure: A Review for its Theories and Applications

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem from the network's flexible structures and parameters, making it highly attractive and applicable across various domains. As the broad learning system (BLS) continues to evolve, DNNs have expanded beyond deep learning (DL), orienting a more comprehensive range of domains. Therefore, this comprehensive review article focuses on two prominent areas where DNN structures have rapidly developed: 1) DL and 2) broad learning. This article provides an in-depth exploration of the techniques related to dynamic construction and inference. Furthermore, it discusses the applications of DNNs in diverse domains while also addressing open issues and highlighting promising research directions. By offering a comprehensive understanding of DNNs, this article serves as a valuable resource for researchers, guiding them toward future investigations.

Go Deep or Broad? Exploit Hybrid Network Architecture for Weakly Supervised Object Classification and Localization

Weakly supervised object classification and localization are learned object classes and locations using only image-level labels, as opposed to bounding box annotations. Conventional deep convolutional neural network (CNN)-based methods activate the most discriminate part of an object in feature maps and then attempt to expand feature activation to the whole object, which leads to deteriorating the classification performance. In addition, those methods only use the most semantic information in the last feature map, while ignoring the role of shallow features. So, it remains a challenge to enhance classification and localization performance with a single frame. In this article, we propose a novel hybrid network, namely deep and broad hybrid network (DB-HybridNet), which combines deep CNNs with a broad learning network to learn discriminative and complementary features from different layers, and then integrates multilevel features (i.e., high-level semantic features and low-level edge features) in a global feature augmentation module. Importantly, we exploit different combinations of deep features and broad learning layers in DB-HybridNet and design an iterative training algorithm based on gradient descent to ensure the hybrid network work in an end-to-end framework. Through extensive experiments on caltech-UCSD birds (CUB)-200 and imagenet large scale visual recognition challenge (ILSVRC) 2016 datasets, we achieve state-of-the-art classification and localization performance.

An integrated BLS-Net optimized with dual PCA and improved PSO-CARS variable selection strategy to determine heavy metals in soil by XRF

Combining machine learning with X-ray fluorescence (XRF) spectroscopy is a promising solution for quantitatively analyzing heavy metal elements in soil. However, the implied linear and nonlinear interferences between spectral intensities and elemental concentrations are difficult to quantify by a single model, thus degrading the prediction performance for low-concentration elements. This paper presents a novel combined spectral variable selection and fusion modeling framework for quantitative analysis of heavy metal elements in soil XRF, which consists of the proposed Particle Swarm Optimization-based Competitive Adaptive Re-weighted Sampling (PSO-CARS) by adaptive decay strategy and Dual Principal Component Analysis-based broad learning system (BLS-Net). The proposed method is compared with advanced machine learning methods by predicting the concentrations of Cr, Cd, Cu, and Pb. The results show that PSO-CARS provides more efficient characteristic and auxiliary spectral variables for BLS-Net. BLS-Net precisely quantified concentrations of heavy metal elements by distinguishing between linear and nonlinear responses in spectra, resulting in average predictive coefficients of determination (R2) exceeding 0.995 of the different elements. The proposed method is a competitive spectral quantitative fusion modeling solution, offering a more precise and reliable analysis of heavy metal elements.

An improved deep convolutional generative adversarial network for quantification of catechins in fermented black tea

The rapid and non-destructive quantification of catechins in fermented black tea is crucial for evaluating the quality of black tea. The combination of hyperspectral imaging and chemometrics has been applied for quantitative detection, but its performance is usually constrained by the limited dataset size. Targeted at the challenge of insufficient samples in regression analysis of catechins, this study proposes an improved deep convolutional generative adversarial network with labeling module, named as DCGAN-L for hyperspectral data augmentation. The DCGAN-L consists of the spectral and label generating modules. First the synthetic spectra were generated, and an indicator was proposed to evaluate their quality. Then, the corresponding label values were generated, including epicatechin gallate (ECG), epicatechin (EGC), catechin (C), and total catechin (CC). For label generating, the Euclidean distances between the synthetic spectrum and all true spectra were measured, followed by allocating weights for calculating the label values based on these distances. Subsequently, the training dataset was augmented with the generated synthetic data. The effect of data augmentation was finally evaluated based on two regression models of random forest (RF) and broad learning system (BLS) for the quantification of catechins. Compared with the results before data augmentation, the average R2 of RF and BLS models increased by 0.044 and 0.164, respectively. The proposed DCGAN-L model allows for the rapid, non-destructive quantification of catechins in black tea in the case of limited sample size.

Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech

Diagnosing Parkinson's disease (PD) via speech is crucial for its non-invasive and convenient data collection. However, the small sample size of PD speech data impedes accurate recognition of PD speech. Therefore, we propose a novel multi-source sparse broad transfer learning (SBTL) method, inspired by incremental broad learning, which balances model learning capability and the overfitting associated with limited sample size of PD speech data. Specifically, SBTL initially leverages a sparse network to preprocess highly overlapping PD speech data, facilitating the identification of intrinsic invariant features between the multi-source auxiliary domain and the target data, which contributes to reducing model complexity. Subsequently, SBTL evaluate transfer effectiveness by virtue of the incremental learning mechanism, adaptively adjusting model structure to ensure the positive transfer of knowledge from the multi-source auxiliary domains to the target domain. Numerous experimental results show that, compared to transfer learning methods for PD diagnosis via speech, SBTL consistently demonstrates significant advantages with a smaller standard deviation, particularly leading by at least 2.58%, 5.71%, 12%, and 14.81% in accuracy, precision, sensitivity, and F1-score, respectively. Even when compared to some well-known transfer learning methods, SBTL still exhibits significant advantages in most cases while maintaining comparable sensitivity. These demonstrate that SBTL is an effective, efficient, and stable multi-source transfer learning method for PD speech recognition, giving more accurate assistance information for clinicians on decision-making for PD in practice.

Extreme Fuzzy Broad Learning System: Algorithm, Frequency Principle, and Applications in Classification and Regression

As an effective alternative to deep neural networks, broad learning system (BLS) has attracted more attention due to its efficient and outstanding performance and shorter training process in classification and regression tasks. Nevertheless, the performance of BLS will not continue to increase, but even decrease, as the number of nodes reaches the saturation point and continues to increase. In addition, the previous research on neural networks usually ignored the reason for the good generalization of neural networks. To solve these problems, this article first proposes the Extreme Fuzzy BLS (E-FBLS), a novel cascaded fuzzy BLS, in which multiple fuzzy BLS blocks are grouped or cascaded together. Moreover, the original data is input to each FBLS block rather than the previous blocks. In addition, we use residual learning to illustrate the effectiveness of E-FBLS. From the frequency domain perspective, we also discover the existence of the frequency principle in E-FBLS, which can provide good interpretability for the generalization of the neural network. Experimental results on classical classification and regression datasets show that the accuracy of the proposed E-FBLS is superior to traditional BLS in handling classification and regression tasks. The accuracy improves when the number of blocks increases to some extent. Moreover, we verify the frequency principle of E-FBLS that E-FBLS can obtain the low-frequency components quickly, while the high-frequency components are gradually adjusted as the number of FBLS blocks increases.

Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction

Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using label enhancement techniques, researchers have computed the probability of a label being ground truth. However, enhancing labels in the noisy label space makes it impossible for the existing partial multilabel label enhancement methods to achieve satisfactory results. Besides, few methods simultaneously involve the ambiguity problem, the feature space's redundancy, and the model's efficiency in PML. To address these issues, this article presents a novel joint partial multilabel framework using broad learning systems (namely BLS-PML) with three innovative mechanisms: 1) a trustworthy label space is reconstructed through a novel label enhancement method to avoid the bias caused by noisy labels; 2) a low-dimensional feature space is obtained by a confidence-based dimensionality reduction method to reduce the effect of redundancy in the feature space; and 3) a noise-tolerant BLS is proposed by adding a dimensionality reduction layer and a trustworthy label layer to deal with PML problem. We evaluated it on six real-world and seven synthetic datasets, using eight state-of-the-art partial multilabel algorithms as baselines and six evaluation metrics. Out of 144 experimental scenarios, our method significantly outperforms the baselines by about 80%, demonstrating its robustness and effectiveness in handling partial multilabel tasks.

An Online Evaluation Method for Random Number Entropy Sources Based on Time-Frequency Feature Fusion

Traditional entropy source evaluation methods rely on statistical analysis and are hard to deploy on-chip or online. However, online detection of entropy source quality is necessary in some applications with high encryption levels. To address these issues, our experimental results demonstrate a significant negative correlation between minimum entropy values and prediction accuracy, with a Pearson correlation coefficient of -0.925 (p-value = 1.07 × 10-7). This finding offers a novel approach for assessing entropy source quality, achieving an accurate rate in predicting the next bit of a random sequence using neural networks. To further improve prediction capabilities, we also propose a novel deep learning architecture, Fast Fourier Transform-Attention Mechanism-Long Short-Term Memory Network (FFT-ATT-LSTM), that integrates a simplified soft attention mechanism with Fast Fourier Transform (FFT), enabling effective fusion of time-domain and frequency-domain features. The FFT-ATT-LSTM improves prediction accuracy by 4.46% and 8% over baseline networks when predicting random numbers. Additionally, FFT-ATT-LSTM maintains a compact parameter size of 33.90 KB, significantly smaller than Temporal Convolutional Networks (TCN) at 41.51 KB and Transformers at 61.51 KB, while retaining comparable prediction performance. This optimal balance between accuracy and resource efficiency makes FFT-ATT-LSTM suitable for online deployment, demonstrating considerable application potential.

Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG

BACKGROUND/OBJECTIVES

Motor neurorehabilitation can be realized by gradually learning diverse motor imagery (MI) tasks. EEG-based brain-computer interfaces (BCIs) provide an effective solution. Nevertheless, existing MI decoding methods cannot balance plasticity for unseen tasks and stability for old tasks. This paper proposes a generative diffusion-based task Incremental Learning (IL) method called GD-TIL.

METHODS

First, data augmentation is employed to increase data diversity by segmenting and recombining EEG signals. Second, to capture temporal-spatial features (TSFs) from different temporal resolutions, a multi-scale temporal-spatial feature extractor (MTSFE) is developed via integrating multiscale temporal-spatial convolutions, a dual-branch pooling operation, multiple multi-head self-attention mechanisms, and a dynamic convolutional encoder. The proposed self-supervised task generalization (SSTG) mechanism introduces a regularization constraint to guide MTSFE and unified classifier updating, which combines labels and semantic similarity between the augmentation with original views to enhance model generalizability for unseen tasks. In the IL phase, a prototype-guided generative replay module (PGGR) is used to generate old tasks' TSFs by training a lightweight diffusion model based on the prototype and label of each task. Furthermore, the generated TSF is merged with a new TSF to fine-tune the convolutional encoder and update the classifier and PGGR. Finally, GD-TIL is evaluated on a self-collected ADL-MI dataset with two MI pairs and a public dataset with four MI tasks.

RESULTS

The continuous decoding accuracy reaches 80.20% and 81.32%, respectively. The experimental results exhibit the excellent plasticity and stability of GD-TIL, even beating the state-of-the-art IL methods.

CONCLUSIONS

Our work illustrates the potential of MI-based BCI and generative AI for continuous neurorehabilitation.

Dynamic Graph Regularized Broad Learning With Marginal Fisher Representation for Noisy Data Classification

Broad learning system (BLS) is an effective neural network requiring no deep architecture, however it is somehow fragile to noisy data. The previous robust broad models directly map features from the raw data, which inevitably learn useless or even harmful features for data representation when the inputs are corrupted by noise and outliers. To address this concern, a discriminative and robust network named as dynamic graph regularized broad learning (DGBL) with marginal fisher representation is proposed for noisy data classification. Different from the previous works, DGBL eliminates the effect of noise before the random feature mapping by the proposed robust and dynamic marginal fisher analysis (RDMFA) algorithm. The RDMFA is able to extract more robust and informative representations for classification from the latent clean data space with dynamically generated graphs. Furthermore, the dynamic graphs learned from RDMFA are incorporated as regularization terms into the objective of DGBL to enhance the discrimination capacity of the proposed network. Extensive quantitative and qualitative experiments conducted on numerous benchmark datasets demonstrate the superiority of the proposed model compared to several state-of-the-art methods.

Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System

Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals is challenging. However, long-duration ECG data are susceptible to various types of noise during acquisition. To tackle the problem, Subspace Search Variational Mode Decomposition (SSVMD) was proposed, which determines the optimal solution by continuously narrowing the parameter subspace and implements data preprocessing by removing baseline drift noise and high-frequency noise modes. In response to the unclear spatial characteristics and excessive data dimension in long-duration ECG data, a Fourier Pooling Broad Learning System (FPBLS) is proposed. FPBLS integrates a Fourier feature layer and a broad pooling layer to express the input data with more obvious features, reducing the data dimension and maintaining effective features. The theory is verified using the MIT-BIH arrhythmia database and achieves better results compared to the latest literature method.

Bidimensionally partitioned online sequential broad learning system for large-scale data stream modeling

Incremental broad learning system (IBLS) is an effective and efficient incremental learning method based on broad learning paradigm. Owing to its streamlined network architecture and flexible dynamic update scheme, IBLS can achieve rapid incremental reconstruction on the basis of the previous model without the entire retraining from scratch, which enables it adept at handling streaming data. However, two prominent deficiencies still persist in IBLS and constrain its further promotion in large-scale data stream scenarios. Firstly, IBLS needs to retain all historical data and perform associated calculations in the incremental learning process, which causes its computational overhead and storage burden to increase over time and as such puts the efficacy of the algorithm at risk for massive or unlimited data streams. Additionally, due to the random generation rule of hidden nodes, IBLS generally necessitates a large network size to guarantee approximation accuracy, and the resulting high-dimensional matrix calculation poses a greater challenge to the updating efficiency of the model. To address these issues, we propose a novel bidimensionally partitioned online sequential broad learning system (BPOSBLS) in this paper. The core idea of BPOSBLS is to partition the high-dimensional broad feature matrix bidimensionally from the aspects of instance dimension and feature dimension, and consequently decompose a large least squares problem into multiple smaller ones, which can then be solved individually. By doing so, the scale and computational complexity of the original high-order model are substantially diminished, thus significantly improving its learning efficiency and usability for large-scale complex learning tasks. Meanwhile, an ingenious recursive computation method called partitioned recursive least squares is devised to solve the BPOSBLS. This method exclusively utilizes the current online samples for iterative updating, while disregarding the previous historical samples, thereby rendering BPOSBLS a lightweight online sequential learning algorithm with consistently low computational costs and storage requirements. Theoretical analyses and simulation experiments demonstrate the effectiveness and superiority of the proposed algorithm.

Semi-supervised contour-driven broad learning system for autonomous segmentation of concealed prohibited baggage items

With the exponential rise in global air traffic, ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security. Although X-ray baggage monitoring is now standard, manual screening has several limitations, including the propensity for errors, and raises concerns about passenger privacy. To address these drawbacks, researchers have leveraged recent advances in deep learning to design threat-segmentation frameworks. However, these models require extensive training data and labour-intensive dense pixel-wise annotations and are finetuned separately for each dataset to account for inter-dataset discrepancies. Hence, this study proposes a semi-supervised contour-driven broad learning system (BLS) for X-ray baggage security threat instance segmentation referred to as C-BLX. The research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage scans. The proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage scans. More specifically, the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues, effectively identifying concealed prohibited items without entire baggage scans. The multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories, including threat and benign classes. The contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation results. The proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation, yielding 90.04%, 78.92%, and 59.44% in terms of mIoU on GDXray, SIXray, and Compass-XP, respectively. Furthermore, the limitations of the proposed system in extracting precise region segments in intricate noisy settings and potential strategies for overcoming them through post-processing techniques were explored (source code will be available at https://github.com/Divs1159/CNN_BLS .).

Deep Optimized Broad Learning System for Applications in Tabular Data Recognition

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data. However, the rapid expansion of big data has resulted in an overwhelming amount of tabular data, necessitating the development of specialized tools for effective management and analysis. This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis. In addition, a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS. The main contributions of this article are: 1) by retracing the network's error from the output space to the latent space, the OBLS adjusts parameters in the feature and enhancement node layers. This process aims to achieve more resilient representations, resulting in improved performance; 2) the DOBLS is a multilayered structure consisting of multiple OBLSs, wherein each OBLS connects to the input and output layers, enabling direct data propagation. This design helps reduce information loss between layers, ensuring an efficient flow of information throughout the network; and 3) the proposed methods demonstrate robustness across various applications, including multiview feature embedding, one-class classification (OCC), camera model identification, electroencephalogram (EEG) signal processing, and radar signal analysis. Experimental results validate the effectiveness of the proposed models. To ensure reproducibility, the source code is available at https://github.com/1027051515/OBLS_DOBLS.

When Broad Learning System Meets Label Noise Learning: A Reweighting Learning Framework

Broad learning system (BLS) is a novel neural network with efficient learning and expansion capacity, but it is sensitive to noise. Accordingly, the existing robust broad models try to suppress noise by assigning each sample an appropriate scalar weight to tune down the contribution of noisy samples in network training. However, they disregard the useful information of the noncorrupted elements hidden in the noisy samples, leading to unsatisfactory performance. To this end, a novel BLS with adaptive reweighting (BLS-AR) strategy is proposed in this article for the classification of data with label noise. Different from the previous works, the BLS-AR learns for each sample a weight vector rather than a scalar weight to indicate the noise degree of each element in the sample, which extends the reweighting strategy from sample level to element level. This enables the proposed network to precisely identify noisy elements and thus highlight the contribution of informative ones to train a more accurate representation model. Thanks to the separability of the model, the proposed network can be divided into several subnetworks, each of which can be trained efficiently. In addition, three corresponding incremental learning algorithms of the BLS-AR are developed for adding new samples or expanding the network. Substantial experiments are conducted to explicate the effectiveness and robustness of the proposed BLS-AR model.

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior performance but centralized training suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, while existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their viability in real-world clinical scenarios. In this article, we propose a lightweight and universal FL framework, named federated deep-broad learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply integrating a pretrained DL feature extractor, a fast and lightweight broad learning inference system with a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6 GB to only 138.4 KB per client using the ResNet-50 backbone at 50-round training. Extensive experiments also show the scalability of FedDBL on model generalization to the unseen dataset, various client numbers, model personalization and other image modalities. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://github.com/tianpeng-deng/FedDBL.

Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning

Classifications of fluids using miniaturized sensors are of substantial importance for various fields of application. Modified with functional nanomaterials, a moisture-enabled electricity generation (MEG) device can execute a dual-purpose operation as both a self-powered framework and a fluid detection platform. In this study, a novel intelligent self-sustained sensing approach was implemented by integrating MEG with deep learning in microfluidics. Following a multilayer design, the MEG device including three individual units for power generation/fluid classification was fabricated in this study by using nonwoven fabrics, hydroxylated carbon nanotubes, poly(vinyl alcohol)-mixed gels, and indium tin bismuth liquid alloy. A composite configuration utilizing hydrophobic microfluidic channels and hydrophilic porous substrates was conducive to self-regulation of the on-chip flow. As a generator, the MEG device was capable of maintaining a continuous and stable power output for at least 6 h. As a sensor, the on-chip units synchronously measured the voltage (V), current (C), and resistance (R) signals as functions of time, whose transitions were completed using relays. These signals can serve as straightforward indicators of a fluid presence, such as the distinctive "fingerprint". After normalization and Fourier transform of raw V/C/R signals, a lightweight deep learning model (wide-kernel deep convolutional neural network, WDCNN) was employed for classifying pure water, kiwifruit, clementine, and lemon juices. In particular, the accuracy of the sample distinction using the WDCNN model was 100% within 15 s. The proposed integration of MEG, microfluidics, and deep learning provides a novel paradigm for the development of sustainable intelligent environmental perception, as well as new prospects for innovations in analytical science and smart instruments.

Textual emotion classification using MPNet and cascading broad learning

As one of the most important tasks of natural language processing, textual emotion classification (TEC) aims to recognize and detect all emotions contained in texts. However, most existing methods are implemented using deep learning approaches, which may suffer from long training time and low convergence. Motivated by these challenges, in this paper, we provide a new solution for TEC by using cascading broad learning (CBL) and sentence embedding using a masked and permuted pre-trained language model (MPNet), named CBLMP. Texts are input into MPNet to generate sentence embedding containing emotional semantic information. CBL is adopted to improve the ability of feature extraction in texts and to enhance model performance for general broad learning, by cascading feature nodes and cascading enhancement nodes, respectively. The L-curve model is adopted to ensure the balance between under-regularization and over-regularization for regularization parameter optimization. Extensive experiments have been carried out on datasets of SMP2020-EWECT and SemEval-2019 Task 3, and the results show that CBLMP outperforms the baseline methods in TEC.

Broad learning system based on maximum multi-kernel correntropy criterion

The broad learning system (BLS) is an effective machine learning model that exhibits excellent feature extraction ability and fast training speed. However, the traditional BLS is derived from the minimum mean square error (MMSE) criterion, which is highly sensitive to non-Gaussian noise. In order to enhance the robustness of BLS, this paper reconstructs the objective function of BLS based on the maximum multi-kernel correntropy criterion (MMKCC), and obtains a new robust variant of BLS (MKC-BLS). For the multitude of parameters involved in MMKCC, an effective parameter optimization method is presented. The fixed-point iteration method is employed to further optimize the model, and a reliable convergence proof is provided. In comparison to the existing robust variants of BLS, MKC-BLS exhibits superior performance in the non-Gaussian noise environment, particularly in the multi-modal noise environment. Experiments on multiple public datasets and real application validate the efficacy of the proposed method.

A NIRS-based recognition of coal and rock using convolution-multiview broad learning system

Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.

A Review of Convex Clustering From Multiple Perspectives: Models, Optimizations, Statistical Properties, Applications, and Connections

Traditional partition-based clustering is very sensitive to the initialized centroids, which are easily stuck in the local minimum due to their nonconvex objectives. To this end, convex clustering is proposed by relaxing K -means clustering or hierarchical clustering. As an emerging and excellent clustering technology, convex clustering can solve the instability problems of partition-based clustering methods. Generally, convex clustering objective consists of the fidelity and the shrinkage terms. The fidelity term encourages the cluster centroids to estimate the observations and the shrinkage term shrinks the cluster centroids matrix so that their observations share the same cluster centroid in the same category. Regularized by the lpn -norm ( pn ∈ {1,2,+∞} ), the convex objective guarantees the global optimal solution of the cluster centroids. This survey conducts a comprehensive review of convex clustering. It starts with the convex clustering as well as its nonconvex variants and then concentrates on the optimization algorithms and the hyperparameters setting. In particular, the statistical properties, the applications, and the connections of convex clustering with other methods are reviewed and discussed thoroughly for a better understanding the convex clustering. Finally, we briefly summarize the development of convex clustering and present some potential directions for future research.

Self-balancing Incremental Broad Learning System with privacy protection

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Modal-Regression-Based Broad Learning System for Robust Regression and Classification

A novel neural network, namely, broad learning system (BLS), has shown impressive performance on various regression and classification tasks. Nevertheless, most BLS models may suffer serious performance degradation for contaminated data, since they are derived under the least-squares criterion which is sensitive to noise and outliers. To enhance the model robustness, in this article we proposed a modal-regression-based BLS (MRBLS) to tackle the regression and classification tasks of data corrupted by noise and outliers. Specifically, modal regression is adopted to train the output weights instead of the minimum mean square error (MMSE) criterion. Moreover, the l2,1 -norm-induced constraint is used to encourage row sparsity of the connection weight matrix and achieve feature selection. To effectively and efficiently train the network, the half-quadratic theory is used to optimize MRBLS. The validity and robustness of the proposed method are verified on various regression and classification datasets. The experimental results demonstrate that the proposed MRBLS achieves better performance than the existing state-of-the-art BLS methods in terms of both accuracy and robustness.

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

The timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature  based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.

This article is categorized under:

A Broad Generative Network for Two-Stage Image Outpainting

Image outpainting is a challenge for image processing since it needs to produce a big scenery image from a few patches. In general, two-stage frameworks are utilized to unpack complex tasks and complete them step-by-step. However, the time consumption caused by training two networks will hinder the method from adequately optimizing the parameters of networks with limited iterations. In this article, a broad generative network (BG-Net) for two-stage image outpainting is proposed. As a reconstruction network in the first stage, it can be quickly trained by utilizing ridge regression optimization. In the second stage, a seam line discriminator (SLD) is designed for transition smoothing, which greatly improves the quality of images. Compared with state-of-the-art image outpainting methods, the experimental results on the Wiki-Art and Place365 datasets show that the proposed method achieves the best results under evaluation metrics: the Fréchet inception distance (FID) and the kernel inception distance (KID). The proposed BG-Net has good reconstructive ability with faster training speed than those of deep learning-based networks. It reduces the overall training duration of the two-stage framework to the same level as the one-stage framework. Furthermore, the proposed method is adapted to image recurrent outpainting, demonstrating the powerful associative drawing capability of the model.

Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System

Machine learning aims to generate a predictive model from a training dataset of a fixed number of known classes. However, many real-world applications (such as health monitoring and elderly care) are data streams in which new data arrive continually in a short time. Such new data may even belong to previously unknown classes. Hence, class-incremental learning (CIL) is necessary, which incrementally and rapidly updates an existing model with the data of new classes while retaining the existing knowledge of old classes. However, most current CIL methods are designed based on deep models that require a computationally expensive training and update process. In addition, deep learning based CIL (DCIL) methods typically employ stochastic gradient descent (SGD) as an optimizer that forgets the old knowledge to a certain extent. In this article, a broad learning system-based CIL (BLS-CIL) method with fast update and high retainability of old class knowledge is proposed. Traditional BLS is a fast and effective shallow neural network, but it does not work well on CIL tasks. However, our proposed BLS-CIL can overcome these issues and provide the following: 1) high accuracy due to our novel class-correlation loss function that considers the correlations between old and new classes; 2) significantly short training/update time due to the newly derived closed-form solution for our class-correlation loss without iterative optimization; and 3) high retainability of old class knowledge due to our newly derived recursive update rule for CIL (RULL) that does not replay the exemplars of all old classes, as contrasted to the exemplars-replaying methods with the SGD optimizer. The proposed BLS-CIL has been evaluated over 12 real-world datasets, including seven tabular/numerical datasets and six image datasets, and the compared methods include one shallow network and seven classical or state-of-the-art DCIL methods. Experimental results show that our BIL-CIL can significantly improve the classification performance over a shallow network by a large margin (8.80%-48.42%). It also achieves comparable or even higher accuracy than DCIL methods, but greatly reduces the training time from hours to minutes and the update time from minutes to seconds.

Dynamic Hybrid Models With Active Sampling and Adaptive Selection of Double-Domain Features for the Tuning of Microwave Cavity Filters

Microwave cavity filters are essential electromechanical coupling devices in communication systems. Structural-parameter tuning by experienced operators improves the filter performance but is demanding and time-consuming. The automatic tuning method has received extensive research attentions using data-driven modeling approaches. However, two main issues affect the accuracy and efficiency of the model construction: 1) features of tuning processes, as model inputs, have limited adaptability and extraction accuracy to different resonant states and 2) models require plentiful training data and the training process is time-consuming. Thus, dynamic hybrid models are developed in this study with self-selected inputs, self-organized samples, and a self-learning structure. First, spatial features are extracted to flexibly depict the tuning characteristic, and double-domain (spatial or circuital) features are selected adaptively to accommodate distinct resonance states. Second, a trustworthiness-curiosity-driven active sampling method is exploited to attain fewer and better-training data. Third, an improved glsms broad learning system acrlong BLS is developed using new modules of incremental node calculation and weight pruning, characterized by more lightweight and flexible structures. The proposed method is effective and flexible demonstrated by simulations and experiments, and the tuning task of microwave cavity filters is fulfilled in a more accurate and efficient manner.

A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.

SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection

Introduction

Epilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved superior performance improvement with its flat structure and less time-consuming training process compared to deep neural networks. Nevertheless, the number of feature and enhancement nodes in GBLS is predetermined. These node settings are also randomly selected and remain unchanged throughout the training process. The characteristic of randomness is thus more easier to make non-optimal nodes generate, which cannot contribute significantly to solving the optimization problem.

Methods

To obtain more optimal nodes for optimization and achieve superior automatic detection performance, we propose a novel broad neural network named self-adaptive evolutionary graph regularized broad learning system (SaE-GBLS). Self-adaptive evolutionary algorithm, which can construct mutation strategies in the strategy pool based on the experience of producing solutions for selecting network parameters, is incorporated into SaE-GBLS model for optimizing the node parameters. The epilepsy seizure is automatic detected by our proposed SaE-GBLS model based on three publicly available EEG datasets and one private clinical EEG dataset.

Results and discussion

The experimental results indicate that our suggested strategy has the potential to perform as well as current machine learning approaches.

Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human-Robot Interaction

A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracted using the broad and deep learning fusion network (BDFN). Considering that the bi-modal emotion is not completely independent of each other, canonical correlation analysis (CCA) is used to analyze and extract the correlation between the emotion features, and a coupling network is established for emotion recognition of the extracted bi-modal features. Both simulation and application experiments are completed. According to the simulation experiments completed on the bimodal face and body gesture database (FABO), the recognition rate of the proposed method has increased by 1.15% compared to that of the support vector machine recursive feature elimination (SVMRFE) (without considering the unbalanced contribution of features). Moreover, by using the proposed method, the multimodal recognition rate is 21.22%, 2.65%, 1.61%, 1.54%, and 0.20% higher than those of the fuzzy deep neural network with sparse autoencoder (FDNNSA), ResNet-101 + GFK, C3D + MCB + DBN, the hierarchical classification fusion strategy (HCFS), and cross-channel convolutional neural network (CCCNN), respectively. In addition, preliminary application experiments are carried out on our developed emotional social robot system, where emotional robot recognizes the emotions of eight volunteers based on their facial expressions and body gestures.

Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder

The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS.

Adaptive soft sensor using stacking approximate kernel based BLS for batch processes

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method-where a fixed-length window slides through the database over time-the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms.

A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.