Particle swarm optimization for solving a class of type-1 and type-2 fuzzy nonlinear equations

• Autorzy: Sadiqbatcha S. , Jafarzadeh S. , Ampatzidis Y.

Identyfikatory

DOI

10.1515/jaiscr-2018-0007

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

type-1 fuzzy sets; type-2 fuzzy sets; polynomial; exponential equation; particle swarm optimization (PSO)

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2018
• Tom: Vol. 8, No. 2
• Strony: 103--110
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Sadiqbatcha S.

• Dept. of Computer and Electrical Engineering California State University, BakersfieldBakersfield, CA, U.S.A.

autor

Jafarzadeh S.

• Dept. of Computer and Electrical Engineering California State University, BakersfieldBakersfield, CA, U.S.A.

autor

Ampatzidis Y.

• Dept. of Physics and Engineering California State University, BakersfieldBakersfield, CA, U.S.A.

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).