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

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  • © 2016

Overview

Authors:
  1. Fernando Gaxiola

    1. Div of Grad Stu, Tijuana Institute of Technology, Tijuana, Mexico

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  2. Patricia Melin

    1. Div of Gdu Stud,CalzTecn sn,Fra.TomAqu, Tijuana Institute of Technology, Tijuana, Mexico

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  3. Fevrier Valdez

    1. Div of graduate studies, Tijuana Institute of Technology, Tijuana, Mexico

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  • Proposes a neural network learning method with type-2 fuzzy weight adjustment
  • Presents a mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights
  • Presents 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 to illustrate the advantages of the proposed method
  • Includes supplementary material: sn.pub/extras

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

Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 1-2

  3. Theory and Background

    • Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 3-20

  4. Problem Statement and Development

    • Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 21-76

  5. Simulations and Results

    • Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 77-97

  6. Conclusions

    • Fernando Gaxiola, Patricia Melin, Fevrier Valdez
    Pages 99-100

  7. Back Matter

    Pages 101-102
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Keywords

About this book

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.

Authors and Affiliations

  • Div of Grad Stu, Tijuana Institute of Technology, Tijuana, Mexico

    Fernando Gaxiola

  • Div of Gdu Stud,CalzTecn sn,Fra.TomAqu, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  • Div of graduate studies, Tijuana Institute of Technology, Tijuana, Mexico

    Fevrier Valdez

Bibliographic Information

  • Book Title: New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

  • Authors: Fernando Gaxiola, Patricia Melin, Fevrier Valdez

  • Series Title: SpringerBriefs in Applied Sciences and Technology

  • DOI: https://doi.org/10.1007/978-3-319-34087-6

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Author(s) 2016

  • Softcover ISBN: 978-3-319-34086-9Published: 09 June 2016

  • eBook ISBN: 978-3-319-34087-6Published: 02 June 2016

  • Series ISSN: 2191-530X

  • Series E-ISSN: 2191-5318

  • Edition Number: 1

  • Number of Pages: IX, 102

  • Number of Illustrations: 94 b/w illustrations

  • Topics: Computational Intelligence, Artificial Intelligence

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