DocumentCode
2611935
Title
A TSK-type fuzzy neural network (TFNN) systems for dynamic systems identification
Author
Lee, Ching-Hung ; Lai, Wei-Yu ; Lin, Yu-Ching
Author_Institution
Dept. of Electr. Eng., Yuan Ze Univ., Taoyuan, Taiwan
Volume
4
fYear
2003
fDate
9-12 Dec. 2003
Firstpage
4002
Abstract
In this paper, a TSK-type fuzzy neural network system (TFNN) for identifying unknown dynamic systems is proposed. The TFNN system can learn its knowledge base from input-output training data. Thus, the unknown system is represented as several if-then rules with TSK-type consequent parts. The TFNN system can be randomly initialized and then trained by the back-propagation algorithm. Several examples are presented to illustrate the effectiveness of our approach.
Keywords
backpropagation; fuzzy neural nets; fuzzy systems; TFNN systems; TSK-type fuzzy neural network; backpropagation algorithm; dynamic systems identification; if-then rules; input-output training data; knowledge base; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Jacobian matrices; Neural networks; Nonlinear systems; Signal processing algorithms; System identification; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2003. Proceedings. 42nd IEEE Conference on
ISSN
0191-2216
Print_ISBN
0-7803-7924-1
Type
conf
DOI
10.1109/CDC.2003.1271776
Filename
1271776
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