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Journal of Information Science and Engineering, Vol. 24 No. 5, pp. 1505-1520 (September 2008)

Efficient Immune-Based Particle Swarm Optimization Learning for Neuro-Fuzzy Networks Design

Cheng-Jian Lin1, Cheng-Hung Chen2 and Chi-Yung Lee3
1Department of Computer Science and Information Engineering
National Chin-Yi University of Technology
Taichung, 411 Taiwan
E-mail: cjlin@
2Department of Electrical and Control Engineering
National Chiao Tung University
Hsinchu, 300 Taiwan
3Department of Computer Science and Information Engineering
Nan Kai Institute of Technology
Nantou, 542 Taiwan

In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for use in TSK-type neuro-fuzzy networks for solving the identification and prediction problems. The proposed IPSO combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. The IA uses the clonal selection principle, such that antibodies between others of high similar degree are affected, and these antibodies, after the process, will have higher quality, accelerating the search and increasing the global search capacity. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. Hence, we employed the advantages of PSO to improve the mutation mechanism of immune algorithm. Experiments with synthetic and real data sets have performed in order to show the applicability of the proposed approach and also to compare with other methods in the literature.

Keywords: neuro-fuzzy network, immune system algorithm, particle swarm optimization, backpropagation, identification, prediction

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Received October 26, 2006; revised April 24, 2007; accepted June 1, 2007.
Communicated by Pau-Choo Chung.