Mamdani and Takagi-Sugeno fuzzy classifier accuracy improvement using enhanced particle swarm optimization

Article type: Research Article

Authors: Elragal, Hassan M.

Affiliations: Electrical Engineering Department, Faculty of Engineering, University of Bahrain, Isa town, Bahrain

Note: [] Corresponding author. Hassan M. Elragal, Electrical Engineering Department, Faculty of Engineering, University of Bahrain, Isa town, Bahrain. E-mail: [email protected]

Abstract: This paper discusses a method for designing a fuzzy-rule-based classifiers using enhanced particle swarm optimization (EPSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_EPSO fuzzy classifier). The second classifier is based on Takagi-Sugeno fuzzy inference system (TS_EPSO fuzzy classifier). The parameters of the proposed fuzzy classifiers including premise (antecedent) parameters, consequent parameters and structure of fuzzy rules are optimized using EPSO. The performances of M_EPSO and TS_EPSO fuzzy classifiers are compared to commonly used fuzzy based classifiers. Experimental results show that higher classification accuracy can be obtained with limited number of fuzzy rules by using the proposed EPSO fuzzy classifiers. Another comparison that shows the consistency of EPSO for optimizing the proposed fuzzy classifiers over other evolutionary algorithms is also presented.

Keywords: Fuzzy classifiers, enhanced particle swarm optimization, optimization of fuzzy system parameters

DOI: 10.3233/IFS-130915

Journal: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 5, pp. 2445-2457, 2014