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Link to original content: https://doi.org/10.1007/s11063-005-1523-4
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State-Space Recurrent Fuzzy Neural Networks for Nonlinear System Identification

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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.

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Authors and Affiliations

  1. Departamento de Control Automatico, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, D.F. 07360, México, México

    Wen Yu

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  1. Wen Yu
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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). https://doi.org/10.1007/s11063-005-1523-4

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