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Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

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Abstract

Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven.

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Authors and Affiliations

  1. Departamento de Control Automatico, CINVESTAV-IPN, Av. IPN 2508, Mexico DF, 07360, Mexico

    Floriberto Ortiz Rodriguez & Wen Yu

  2. Centro de Investigación en Computación-IPN, AV. Juan de Dios Bátiz S/N, Unidad Profesional “Adolfo López Mateos”, Mexico DF, C. P. 07738, Mexico

    Marco A. Moreno-Armendariz

Authors
  1. Floriberto Ortiz Rodriguez
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  2. Wen Yu
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  3. Marco A. Moreno-Armendariz
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Correspondence to Wen Yu.

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Rodriguez, F.O., Yu, W. & Moreno-Armendariz, M.A. Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC. Neural Process Lett 28, 49–62 (2008). https://doi.org/10.1007/s11063-008-9081-1

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  • DOI: https://doi.org/10.1007/s11063-008-9081-1

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