SaFIN: a self-adaptive fuzzy inference network

Dynamic Residential Energy Management for Real-Time Pricing

A home energy management system (HEMS) was designed in this paper for a smart home that uses integrated energy resources such as power from the grid, solar power generated from photovoltaic (PV) panels, and power from an energy storage system (ESS). A fuzzy controller is proposed for the HEMS to optimally manage the integrated power of the smart home. The fuzzy controller is designed to control the power rectifier for regulating the AC power in response to the variations in the residential electric load, solar power from PV panels, power of the ESS, and the real-time electricity prices. A self-learning scheme is designed for the proposed fuzzy controller to adapt with short-term and seasonal climatic changes and residential load variations. A parsimonious parameterization scheme for both the antecedent and consequent parts of the fuzzy rule base is utilized so that the self-learning scheme of the fuzzy controller is computationally efficient.

Video-Based Vehicle Detection Approach with Data-Driven Adaptive Neuro-Fuzzy Networks

This paper proposes a novel video-based vehicle detection approach with data-driven adaptive neuro-fuzzy networks. The key ideas include configuring several virtual loops as vehicle detection zones in the image, assuming moving vehicles will cause pixel intensities and local textures to change, and then identifying such changes to detect vehicles. In this work, vehicle detection is treated as a pattern classification problem. First, 14 image features (regarding foreground area, texture change, and environmental condition) are extracted to represent the distinction between vehicle and nonvehicle patterns. Then, three neuro-fuzzy networks are trained via incremental semi-supervised learning to build a data-driven adaptive classifier, which judges whether a vehicle is located in the virtual loop. The semi-supervised learning procedure is performed based on a modified tri-training approach, to automatically optimize the structures and parameters of the component neuro-fuzzy networks. Experimental results illustrate that the proposed approach is accurate and robust to detect vehicles in complex environments (e.g. adverse illumination and weather conditions), and thus can improve the performance of video-based vehicle detection.

Simplified interval type-2 fuzzy neural networks

This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.

Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm

In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.