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Higher Degree Fuzzy Transform: Application to Stationary Processes and Noise Reduction Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Particle Swarm Optimization with Fuzzy Dynamic Parameters Adaptation for Modular Granular Neural Networks

Authors : Daniela Sánchez, Patricia Melin, Oscar Castillo

Published in: Advances in Fuzzy Logic and Technology 2017

Publisher: Springer International Publishing

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Abstract

In this paper a new method for Modular Granular Neural Network (MGNN) optimization with a granular approach is presented. A Particle Swarm Optimization technique is proposed to perform the granulation of information with a fuzzy dynamic parameters adaptation to prevent stagnation. The proposed fuzzy inference system seeks to adjust some PSO parameters such as w, C1 and C2 to ensure that the parameters have adequate values depending on the current behavior of the particles. The objective of the proposed PSO is design optimal MGNN architectures. The modular granular neural networks are applied to human recognition based on iris biometrics, where a benchmark database is used and the objective function in this work is the minimization of the error of recognition.

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Metadata
Title
Particle Swarm Optimization with Fuzzy Dynamic Parameters Adaptation for Modular Granular Neural Networks
Authors
Daniela Sánchez
Patricia Melin
Oscar Castillo
Copyright Year
2018
Book
Advances in Fuzzy Logic and Technology 2017

Print ISBN: 978-3-319-66826-0

Electronic ISBN: 978-3-319-66827-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-66827-7_25

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