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Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning with Gaussian Membership Functions

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Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 547))

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Abstract

In this chapter type-1 and type-2 fuzzy inferences systems are used to obtain the type-1 or type-2 fuzzy weights in the connection between the layers of a neural network. We used two type-1 or type-2 fuzzy systems that work in the backpropagation learning method with the type-1 or type-2 fuzzy weight adjustment. The mathematical analysis of the proposed learning method architecture and the adaptation of type-1 or 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 neural networks with type-1 fuzzy weights or type-2 fuzzy weights are presented. The proposed approach is applied to the case of Mackey–Glass time series prediction.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    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 Patricia Melin .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Gaxiola, F., Melin, P., Valdez, F. (2014). Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning with Gaussian Membership Functions. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-05170-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05169-7

  • Online ISBN: 978-3-319-05170-3

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