DocumentCode
3180225
Title
System identification with state-space recurrent fuzzy neural networks
Author
Yu, Wen ; Ferreyra, Andrés
Author_Institution
Dept. de Control Automatico, CINVESTAV-IPN, Mexico City, Mexico
Volume
5
fYear
2004
fDate
14-17 Dec. 2004
Firstpage
5106
Abstract
In this paper, we propose a new recurrent fuzzy neural networks, which has the standard state space form, we call it state-space recurrent neural networks. Input-to-state stability is applied to access robust training algorithms for system identification. Stable learning algorithms for the premise part and the consequence part of fuzzy rules are proved.
Keywords
fuzzy neural nets; identification; learning (artificial intelligence); recurrent neural nets; state-space methods; fuzzy rules; input-to-state stability; robust training algorithms; stable learning algorithms; state-space recurrent fuzzy neural networks; system identification; Backpropagation algorithms; Function approximation; Fuzzy neural networks; Fuzzy systems; Neural networks; Neurofeedback; Recurrent neural networks; Robust stability; Robustness; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2004. CDC. 43rd IEEE Conference on
ISSN
0191-2216
Print_ISBN
0-7803-8682-5
Type
conf
DOI
10.1109/CDC.2004.1429617
Filename
1429617
Link To Document