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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Vydané v:	IEEE transactions on fuzzy systems Ročník 30; číslo 9; s. 3574 - 3588
Hlavní autori:	Han, Honggui, Sun, Chenxuan, Wu, Xiaolong, Yang, Hongyan, Qiao, Junfei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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