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State-Space Recurrent Fuzzy Neural Networks for Nonlinear System Identification

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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.

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Correspondence to Wen Yu.

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Yu, W. State-Space Recurrent Fuzzy Neural Networks for Nonlinear System Identification. Neural Process Lett 22, 391–404 (2005).

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