Comparison between multiobjective GA and PSO for parameter optimization of AT2-FLC for a real application in FPGA

Author

Maldonado, Yazmin ; Castillo, Oscar

Author_Institution

Div. of Grad. Studies, Tijuana Inst. of Technol., Tijuana, Mexico

fYear

2012

fDate

6-8 Aug. 2012

Firstpage

1

Lastpage

6

Abstract

This paper describes the design of a type-2 average fuzzy system on FPGAs and its optimization using multiobjective Particle Swarm Optimization (PSO) and a multiobjective Genetic Algorithm (GA) for the regulation of speed of a DC motor. Based on the concept of evolution, the PSO algorithm and GA are applied to membership functions parameter optimization of type-2 average fuzzy inference systems. Implementations and simulations are carried out in FPGA using the Xilinx system generator. The optimization method was coded in Matlab. The results of comparison PSO with GA were analyzed statistically.

Keywords

DC motors; field programmable gate arrays; fuzzy logic; fuzzy reasoning; genetic algorithms; particle swarm optimisation; AT2-FLC; DC motor speed regulation; FPGA; Matlab; PSO; Xilinx system generator; average type-2 fuzzy logic system; membership function parameter optimization; multiobjective GA; multiobjective genetic algorithm; multiobjective particle swarm optimization; type-2 average fuzzy inference systems; type-2 average fuzzy system; Equations; Field programmable gate arrays; Fuzzy systems; Genetic algorithms; Mathematical model; Optimization; Uncertainty; AT2-FLC; FPGA; GA; PSO; ReSDCM; T2-MF;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American

Conference_Location

Berkeley, CA

ISSN

pending

Print_ISBN

978-1-4673-2336-9

Electronic_ISBN

pending

Type

conf

DOI

10.1109/NAFIPS.2012.6291047

Filename

6291047