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Title: Efficient Cooperative Particle Swarm Optimization for TSK-Type Neural Fuzzy Systems and Its Classification Applications
Authors: Cheng-Hung Chen
Journal: Journal of advances in applied & computational mathematics
Year: 2015
Volume: 2
Issue: 1
Language: en
DOI: 10.15377/2409-5761.2014.02.01.6
Keywords: Particle Swarm Optimizationclassification.cooperative evolutionTSK-type neural fuzzy systems
This study proposes a cooperative particle swarm optimization (CPSO) to optimize the parameters of the TSK-type neural fuzzy system (TNFS) for classification applications. The proposed CPSO uses cooperative behavior among multiple subswarms to decompose the neural fuzzy systems into rule-based subswarms, and each particle within each subswarm evolves by a specific particle swarm optimization (PSO) separately. Therefore, the CPSO can accelerate the search and increase global search capacity. Finally, the TNFS with CPSO (TNFS-CPSO) is adopted in several classification applications. Experimental results demonstrate that the proposed TNFS-CPSO method has a higher accuracy rate and a faster convergence rate than the other methods.
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