System identification with state-space recurrent fuzzy neural networks
Wen Yu, Andrés Ferreyra-Ramírez · 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) · 2004
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.