Set-Membership Estimation for Complex Networks Subject to Linear and Nonlinear Bounded Attacks

A data-driven guaranteed zonotopic estimation for unknown time-varying system

Time-varying system is a widespread class of systems in reality. Model of such system is difficult to obtain because the parameters vary with time. Hence, a data-driven state estimation for unknown discrete time-varying system is investigated with zonotopic reachability analysis. Since the system model is unknown, a time-varying matrix zonotope which contains all possible models is computed by the available prior input-state trajectories. On this basis, an over-approximated reachable zonotope of system state is obtained through iteration. Then, the boundedness of over-approximated reachable zonotope is proved by introducing P-radius definition. This method guarantees that the actual system state falls within the estimation range and maintains the compactness of the estimation result. Finally, a numerical example and a circuit system simulation show the validity and applicability of the proposed method.

Finite-Horizon H∞ State Estimation for Complex Networks With Uncertain Couplings and Packet Losses: Handling Amplify-and-Forward Relays

This article is concerned with the state estimation problem for a class of complex networks (CNs) with uncertain inner couplings and packet losses over communication networks. The inner couplings are allowed to be uncertain and varying in a specific interval. The amplify-and-forward (AaF) relay protocols are introduced to improve the communication quality and enhance the propagation distance. The Bernoulli random variables are used to characterize the randomly occurring packet losses encountered in communication channels. The focus of this article is on the design of a state estimator for each node of CNs such that a prescribed performance constraint is satisfied for the dynamical error system over a finite horizon. A sufficient condition is first provided to verify the existence of the desired state estimator, and the estimator gain is then determined by solving two coupled backward Riccati difference equations (RDEs). Subsequently, a recursive state estimation algorithm is put forward that is suitable for online computation. Finally, a numerical example is given to demonstrate the effectiveness of the proposed estimation method.

Maximum Correntropy Filtering for Complex Networks With Uncertain Dynamical Bias: Enabling Componentwise Event-Triggered Transmission

This article is concerned with the maximum correntropy filtering (MCF) problem for a class of nonlinear complex networks subject to non-Gaussian noises and uncertain dynamical bias. With aim to utilize the constrained network bandwidth and energy resources in an efficient way, a componentwise dynamic event-triggered transmission (DETT) protocol is adopted to ensure that each sensor component independently determines the time instant for transmitting data according to the individual triggering condition. The principal purpose of the addressed problem is to put forward a dynamic event-triggered recursive filtering scheme under the maximum correntropy criterion, such that the effects of the non-Gaussian noises can be attenuated. In doing so, a novel correntropy-based performance index (CBPI) is first proposed to reflect the impacts from the componentwise DETT mechanism, the system nonlinearity, and the uncertain dynamical bias. The CBPI is parameterized by deriving upper bounds on the one-step prediction error covariance and the equivalent noise covariance. Subsequently, the filter gain matrix is designed by means of maximizing the proposed CBPI. Finally, an illustrative example is provided to substantiate the feasibility and effectiveness of the developed MCF scheme.

TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging

Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those methods: 1) effectively capturing salient waveforms in sleep signals and 2) correctly classifying confusing stages in transitioning epochs. In this study, we propose a novel deep neural-network structure, TransSleep, that captures distinctive local temporal patterns and distinguishes confusing stages using two auxiliary tasks. In particular, TransSleep captures salient waveforms in sleep signals by an attention-based multiscale feature extractor and correctly classifies confusing stages in transitioning epochs, while modeling contextual relationships with two auxiliary tasks. Results show that TransSleep achieves promising performance in automatic sleep staging. The validity of TransSleep is demonstrated by its state-of-the-art performance on two publicly available datasets: 1) Sleep-EDF and 2) MASS. Furthermore, we performed ablations to analyze our results from different perspectives. Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep-learning-based sleep staging.

Finite-Time H∞ State Estimation for Two-Time-Scale Complex Networks Under Stochastic Communication Protocol

The issue of finite-time H_∞ state estimation is studied for a class of discrete-time nonlinear two-time-scale complex networks (TTSCNs) whose measurement outputs are transmitted to a remote estimator via a bandwidth-limited communication network under the stochastic communication protocol (SCP). To reflect different time scales of state evolutions, a new discrete-time TTSCN model is devised by introducing a singular perturbation parameter (SPP). For the sake of avoiding/alleviating the undesirable data collisions, the SCP is adopted to schedule the data transmissions, where the transition probabilities involved are assumed to be partially unknown. By constructing a new Lyapunov function dependent on the information of the SCP and SPP, a sufficient condition is derived which ensures that the resulting error dynamics is stochastically finite-time bounded and satisfies a prescribed H_∞ performance index. By resorting to the solutions of several matrix inequalities, the gain matrices of the state estimator are given and the admissible upper bound of the SPP can be evaluated simultaneously. The performance of the designed state estimator is demonstrated by two examples.