Recurrent fuzzy neural networks for nonlinear system identification
Wen Yu, Xiaoou Li · 2007
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.