A Comparative Study of Space Search Algorithm and Particle Swarm Optimization in the Design of ANFIS-based Fuzzy Models

Majlesi Journal of Electrical Engineering

مقاله 4، دوره 5، شماره 1، خرداد 2011   

نوع مقاله: Review Article

شناسه دیجیتال (DOI): 10.1234/mjee.v5i1.366

نویسندگان

Wei Huang؛ Lixin Ding؛ Sung-Kwun Oh^*

The University of Suwon

چکیده

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of ANFIS-based fuzzy models based on SSA and information granulation (IG). In comparison with “conventional” evolutionary algorithms (such as PSO), SSA leads not only to better search performance to find global optimization but is also more computationally effective. In the hybrid optimization of ANFIS-based fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of ANFIS-based fuzzy models comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using three representative numerical examples such as Non-linear function, gas furnace, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some “conventional” fuzzy models already encountered in the literature.

کلیدواژه‌ها

Space Search Algorithm؛ en؛ Particle swarm Algorithm؛ Information Granulation؛ ANFIS-based Fuzzy Inference System

مراجع

[1] M.C. Nataraja, M.A. Jayaram, and C.N. Ravikumar, “Prediction of Early Strength of Concrete: A Fuzzy Inference System Model,” International Journal of Physical Sciences, Vol. 1, pp. 47-56, 2006.

[2] W. Pedrycz, “An identification algorithm in fuzzy relational system,” Fuzzy Sets Syst., Vol. 13, pp. 153-167, 1984.

[3] R. M. Tong, “The evaluation of fuzzy models derived from experimental data,” Fuzzy Sets Syst., Vol. 13, pp. 1-12, 1980.

[4] C. W. Xu. and Y. Zailu, “Fuzzy model identification self-learning for dynamic system,” IEEE Trans. on System, Man, and Cybernetics, Vol. 17(4), pp. 683-689, 1987.

[5] M. Sugeno and T. Yasukawa, “Linguistic modeling based on numerical data,” in IFSA’91 Brussels, Computer, Management & System Science, pp. 264-267. 1991.

[6] S. K. Oh. and W. Pedrycz, “Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems,” Fuzzy Sets and Syst., Vol. 115(2), pp. 205-230, 2000.

[7] F. Liu, P. Lu, and R. Pei, “A new fuzzy modeling and identification based on fast-cluster and genetic algorithm,” Intell. Contr. Automat., Vol. 1, pp. 290-293, 2004.

[8] W.Y Chung, W. Pedrycz, and E.T. Kim, “A new two-phase approach to fuzzy modeling for nonlinear function approximation,” IEICE Trans. Info. Syst., Vol. 9, pp. 2473-2483, 2006

[9] Z.-L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system, ” IEEE Trans. Energy Conversion, Vol. 19, pp. 384-391, 2004.

[10] K.E. Parsopoulos and M.N. Vrahatis, “On the computation of all global minimizers through particle swarm optimization,” IEEE Trans. Evolutionary Computation, Vol. 8, pp. 211-224, 2004.

[11] M.A. Abido, “Optimal design of power-system stabilizers using particle swarm optimization, ” IEEE Trans. Energy Conversion, Vol. 17, pp. 406-413, 2002.

[12] C.-L. Huang and C.-J. Wang, “A GA-based feature selection and parameters optimization for support vector machines, ” Expert Systems with Applications, Vol. 31, pp. 231-240, 2006.

[13] B. J. Park., W. Pedrycz., and S. K. Oh, “Identification of Fuzzy Models with the Aid of Evolutionary Data Granulation”. IEE Proc.-Control Theory and Applications, Vol. 148, pp. 406-418, 2001

[14] S. Horikawa, T. Furuhashi and Y. Uchigawa, “On fuzzy modeling using fuzzy neural networks with the back propagation algorithm,” IEEE Trans. Neural Networks, Vol. 3, pp. 801-806, 1992.

[15] G. Kang and M. Sugeno, “Fuzzy modeling,” Trans. SICE, Vol. 23, pp. 106-108, 1987.

[16] T. Kondo, “Revised GMDH algorithm estimating degree of the complete polynomial,” Trans. Soc. Instrum. Control Eng., Vol. 22(9), pp. 928-934, 1986.

[17] S.K. Oh, W. Pedrycz, and H.S. Park, “Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation,” Knowledge-Based systems, Vol. 17, pp. 1-13, 2004.

[18] Krishnaiah, and P.R. Kanal, editors: Classification, Pattern Recognition, and Reduction of Dimensionality. Handbook of Statistics, Vol. 2, North-Holland, Amsterdam, 1982.

[19] L.X. Wang and M. Mendel: “Generating fuzzy rules from numerical data with applications,” IEEE Trans. on System, Man, and Cybernetics, Vol. 22, pp. 1414-1427 (1992)

[20] J.S.R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans. System Man Cybernet., Vol. 23(3), pp. 665-685, 1993.

[21] L.P. Maguire, B. Roche, T.M. McGinnity, and L.J. McDaid, “Predicting a chaotic time series using a fuzzy neural, network,” Inform. Sci., Vol. 112, pp. 125-136, 1998.

[22] J.C. Duan and F.L. Chung, “Multilevel fuzzy relational systems: structure and identification,” Soft Computing, Vol. 6, pp. 71-86, 2002.

[23] Y.Chen, B. Yang, and A. Abraham, “Automatic design of hierarchical Takagi-Sugeno type fuzzy systems using evolutionary algorithms,” IEEE Trans. Fuzzy Systems, Vol. 15, pp. 385-397, 2007.