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Efficient Parametric Adjustment of Fuzzy Inference System Using Error Backpropagation Method

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Book cover Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

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Abstract

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, CP 359, CEP 13566.590, University of São Paulo, São Carlos, SP, Brazil

    Ivan da Silva & Rogerio Flauzino

Authors
  1. Ivan da Silva
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  2. Rogerio Flauzino
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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da Silva, I., Flauzino, R. (2009). Efficient Parametric Adjustment of Fuzzy Inference System Using Error Backpropagation Method. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_83

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  • DOI: https://doi.org/10.1007/978-3-642-04274-4_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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