Adaptive nonlinear control using TSK-type recurrent fuzzy neural network system

Ching Hung Lee*, Ming Hui Chiu

*Corresponding author for this work

研究成果: Conference contribution同行評審

10 引文 斯高帕斯(Scopus)

摘要

This paper presents a TSK-type recurrent fuzzy neural network (TRFNN) system and hybrid algorithm to control nonlinear uncertain systems. The TRFNN is modified from the RFNN to obtain generalization and fast convergence rate. The consequent part is replaced by linear combination of input variables and the internal variable- fire strength is feedforward to output to increase the network ability. Besides, a hybrid learning algorithm (GA_BPPSO) is proposed to increase the convergence, which combines the genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO). Several simulation results are proposed to show the effectiveness of TRFNN system and GA_BPPSO algorithm.

原文English
主出版物標題Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
頁面38-44
頁數7
版本PART 1
出版狀態Published - 2007
事件4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China
持續時間: 3 六月 20077 六月 2007

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 1
4491 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference4th International Symposium on Neural Networks, ISNN 2007
國家China
城市Nanjing
期間3/06/077/06/07

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