System identification with state-space recurrent fuzzy neural networks

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.