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

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

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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fernando Gaxiola
    • 1
  • Patricia Melin
    • 1
  • Fevrier Valdez
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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