Training Fuzzy Neural Network via Multiobjective Optimization for Nonlinear Systems Identification

The design of a fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train an FNN, which may easily result in overfitting phenomenon to degrade the generalization performance. To improve the generalization performance, an FNN with a multiob...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 30; H. 9; S. 3574 - 3588
Hauptverfasser:	Han, Honggui, Sun, Chenxuan, Wu, Xiaolong, Yang, Hongyan, Qiao, Junfei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation Approximation algorithms Artificial neural networks Complexity theory Continuity (mathematics) Convergence Fuzzy control Fuzzy logic fuzzy neural network (FNN) Fuzzy neural networks generalization performance Machine learning multiobjective particle swarm optimization (PSO) algorithm Multiple objective analysis Neural networks Nonlinear systems Optimization Smoothness System effectiveness Training
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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