New backpropagation algorithm with type-2 fuzzy weights for neural networks [electronic resource] / Fernando Gaxiola, Patricia Melin, Fevrier Valdez.
2016
Q325.78
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Title
New backpropagation algorithm with type-2 fuzzy weights for neural networks [electronic resource] / Fernando Gaxiola, Patricia Melin, Fevrier Valdez.
Author
Gaxiola, Fernando, author.
ISBN
9783319340876 (electronic book)
3319340875 (electronic book)
9783319340869
3319340875 (electronic book)
9783319340869
Published
Switzerland : Springer, 2016.
Language
English
Description
1 online resource (ix, 102 pages) : illustrations.
Call Number
Q325.78
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 13, 2016).
Series
SpringerBriefs in applied sciences and technology. Computational intelligence.
Available in Other Form
New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
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Table of Contents
Introduction.-Theory and Background
Problem Statement an Development
Simulations and Results
Conclusions.
Problem Statement an Development
Simulations and Results
Conclusions.