Title :
Recurrent fuzzy neural networks for nonlinear system identification
Author :
Yu, Wen ; Li, XiaoOu
Author_Institution :
CINVESTAV-IPN, Mexico
Abstract :
In this paper, we propose a new recurrent fuzzy neural network, 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 :
identification; neurocontrollers; nonlinear control systems; recurrent neural nets; state-space methods; fuzzy rules; input-to-state stability; nonlinear system identification; recurrent fuzzy neural networks; robust training algorithms; Backpropagation algorithms; Fuzzy neural networks; Fuzzy systems; Neural networks; Neurofeedback; Nonlinear systems; Robustness; Stability; State-space methods; System identification;
Conference_Titel :
Intelligent Control, 2007. ISIC 2007. IEEE 22nd International Symposium on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0440-7
Electronic_ISBN :
2158-9860
DOI :
10.1109/ISIC.2007.4450952
