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
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;
Conference_Titel :
Decision and Control, 2004. CDC. 43rd IEEE Conference on
Print_ISBN :
0-7803-8682-5
DOI :
10.1109/CDC.2004.1429617
