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Genetic Optimization of Type-2 Fuzzy Weight Adjustment for Backpropagation in Ensemble Neural Network

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Recent Advances on Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 451))

Abstract

In this paper a genetic algorithm is used to optimize the three neural networks forming an ensemble. Genetic algorithms are also used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on recent methods that handle weight adaptation and especially fuzzy weights. In this work an ensemble neural network of three neural networks and average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, México

    Fernando Gaxiola, Patricia Melin & Fevrier Valdez

Authors
  1. Fernando Gaxiola
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  2. Patricia Melin
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  3. Fevrier Valdez
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Corresponding author

Correspondence to Fernando Gaxiola .

Editor information

Editors and Affiliations

  1. Chula Vista, 91909, California, USA

    Oscar Castillo

  2. Chula Vista, 91909, California, USA

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warszawa, 01-447, Poland

    Janusz Kacprzyk

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Gaxiola, F., Melin, P., Valdez, F. (2013). Genetic Optimization of Type-2 Fuzzy Weight Adjustment for Backpropagation in Ensemble Neural Network. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_13

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  • DOI: https://doi.org/10.1007/978-3-642-33021-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33020-9

  • Online ISBN: 978-3-642-33021-6

  • eBook Packages: EngineeringEngineering (R0)

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