Efficient Cooperative Particle Swarm Optimization for TSK-Type Neural Fuzzy Systems and Its Classification Applications


Particle swarm optimization
cooperative evolution
TSK-type neural fuzzy systems

How to Cite

Chen, C.-H. (2015). Efficient Cooperative Particle Swarm Optimization for TSK-Type Neural Fuzzy Systems and Its Classification Applications. Journal of Advances in Applied &Amp; Computational Mathematics, 2(1), 30–37. https://doi.org/10.15377/2409-5761.2014.02.01.6


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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