Keywords
cooperative evolution
TSK-type neural fuzzy systems
classification.
How to Cite
Abstract
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.References
Duda PO, Hart PE. Pattern Classification and Scene Analysis. New York: Wiley 1973.
Nauck D, Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Set Syst 1997; 89(3): 277-288. http://dx.doi.org/10.1016/S0165-0114(97)00009-2
Ishibuchi H, Nakashima T, Murata T. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE T Syst Man Cy B 1999; 29(5): 601-618. http://dx.doi.org/10.1109/3477.790443
Kasabov N. Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE T Syst Man Cy B 2001; 31(6): 902-918. http://dx.doi.org/10.1109/3477.969494
Ajiboye AB, Weir RF. A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control. IEEE T Neur Sys Reh 2005; 13(3): 280-291. http://dx.doi.org/10.1109/TNSRE.2005.847357
Lin CJ, Xu YJ. Efficient reinforcement learning through dynamic symbiotic evolution for TSK-type fuzzy controller design. Int J Gen Syst 2005; 34(5): 559-578. http://dx.doi.org/10.1080/03081070500132377
Ducange P, Lazzerini B, Marcelloni F. Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets. Soft Comput 2010; 14(7): 713-728. http://dx.doi.org/10.1007/s00500-009-0460-y
He C, Ye Y. Evolution computation based learning algorithms of polygonal fuzzy neural networks. Int J Intell Syst 2011; 26(4): 340-352. http://dx.doi.org/10.1002/int.20469
Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. IEEE T Syst Man Cy 1993; 23(3): 665-685. http://dx.doi.org/10.1109/21.256541
Juang CF, Lin CT. An online self-constructing neural fuzzy inference network and its applications. IEEE T Fuzzy Syst 1998; 6(1): 12-31. http://dx.doi.org/10.1109/91.660805
Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proc. 6th Int Symp MicroMach Hum Sci 1995; pp 39-43.
Kennedy J, Eberhart R. Particle swarm optimization. In: Proc IEEE Int Conf Neural Netw 1995; pp 1942-1948.
Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y. A particle swarm optimization for reactive power and voltage control considering voltage security assessmen. IEEE T Power Syst 2000; 15(4): 1232-1239. http://dx.doi.org/10.1109/59.898095
Gaing ZL. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE T Energy Conver 2004; 19(2): 384-391. http://dx.doi.org/10.1109/TEC.2003.821821
Feng HM, Chen CY, Ye F. Adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm. Cybernet Syst 2006; 37(5): 463-479. http://dx.doi.org/10.1080/01969720600683429
Huang VL, Suganthan PN, Liang JJ. Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems. Int J Intell Syst 2006; 21(2): 209-226. http://dx.doi.org/10.1002/int.20128
Cai X, Zhang N, Venayagamoorthy GK, Wunsch DC. Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm. Neurocomputing 2007; 70(13-15): 2342-2353. http://dx.doi.org/10.1016/j.neucom.2005.12.138
Song Y, Chen Z, Yuan Z. New chaotic PSO-based neural network predictive control for nonlinear process. IEEE T Neur Net Lear 2007; 18(2): 595-601. http://dx.doi.org/10.1109/TNN.2006.890809
Zhang JR, Zhang J, Lok TM, Lyu MR. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl Math Comput 2007; 185(2): 1026-1037. http://dx.doi.org/10.1016/j.amc.2006.07.025
del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG. Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE T Evol Comput 2008; 12(2): 171-195. http://dx.doi.org/10.1109/TEVC.2007.896686
Lin CJ, Peng CC. Chord recognition using neural networks based on particle swarm optimization. Cybernet Syst 2011; 42(4): 264-282. http://dx.doi.org/10.1080/01969722.2011.583597
Potter MA, De Jong KA. A cooperative coevolutionary approach to function optimization. In: Proc. of the Third Conference on Parallel Problem Solving from Nature 1994; pp 249-257. http://dx.doi.org/10.1007/3-540-58484-6_269
Potter MA, De Jong KA. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 2000; 8(1): 1-29. http://dx.doi.org/10.1162/106365600568086
Yang Z, Tang K, Yao X. Large scale evolutionary optimization using cooperative coevolution. Inform Sciences 2008; 178(15): 2985-2999. http://dx.doi.org/10.1016/j.ins.2008.02.017
Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE T Syst Man Cybern 1985; 1(1): 116-132. http://dx.doi.org/10.1109/TSMC.1985.6313399
Lin CT, Lee CSG. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System. NJ: Prentice-Hall 1996.
Shi Y, Eberhart RC. A modified particle swarm optimizer. In: Proc. of IEEE International Conference on Evolutionary Computation, Anchorage, AK, 1998; pp 69-73.
Eberhart RC, Shi Y. Tracking and optimizing dynamic systems with particle swarms. In: Proc. of IEEE Congress on Evolutionary Computation, Seoul, Korea, 2001; pp 94-97.
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