New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

  • Fernando Gaxiola
  • Patricia Melin
  • Fevrier Valdez

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Intelligence book sub series (BRIEFSINTELL)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 1-2
  3. Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 3-20
  4. Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 21-76
  5. Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 77-97
  6. Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 99-100
  7. Back Matter
    Pages 101-102

About this book

Introduction

In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. 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 research of recent methods that handle weight adaptation and especially fuzzy weights.
The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.
The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.
The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.

Keywords

Computational Intelligence Neural Networks Type-2 Fuzzy Weight Back-propagation Algorithm for Neural Networks Fuzziness

Authors and affiliations

  • Fernando Gaxiola
    • 1
  • Patricia Melin
    • 2
  • Fevrier Valdez
    • 3
  1. 1.Div of Grad StuTijuana Institute of TechnologyTijuanaMexico
  2. 2.Div of Gdu Stud,CalzTecn sn,Fra.TomAquTijuana Institute of TechnologyTijuanaMexico
  3. 3.Div of graduate studiesTijuana Institute of TechnologyTijuanaMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-34087-6
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-34086-9
  • Online ISBN 978-3-319-34087-6
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • About this book
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