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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

Sheriff Sadiqbatcha
  • Dept. of Computer and Electrical Engineering, California State University, Bakersfield, CA, United States of America
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/ Saeed Jafarzadeh
  • Dept. of Computer and Electrical Engineering, California State University, Bakersfield, CA, United States of America
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/ Yiannis Ampatzidis
  • Dept. of Physics and Engineering, California State University, Bakersfield, CA, United States of America
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Published Online: 2017-11-01 | DOI: https://doi.org/10.1515/jaiscr-2018-0007

Abstract

This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.

Keywords: type1 and type2 fuzzy sets; polynomial and exponential equations; particle swarm optimization

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About the article

Received: 2017-01-30

Accepted: 2017-03-31

Published Online: 2017-11-01

Published in Print: 2018-04-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 8, Issue 2, Pages 103–110, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2018-0007.

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© 2018 Sheriff Sadiqbatcha et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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