New backpropagation algorithm with type-2 fuzzy weights for neural networks [electronic resource] / Fernando Gaxiola, Patricia Melin, Fevrier Valdez.
Gaxiola, Fernando, author.; Melin, Patricia, 1962- author.; Valdez, Fevrier, author.
2016
Q325.78
Available Online

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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
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.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 13, 2016).
Added Author Melin, Patricia, 1962- author.
Valdez, Fevrier, author.
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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Introduction.-Theory and Background
Problem Statement an Development
Simulations and Results
Conclusions.

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