Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of Fuzzy Models with the Aid of an Improved Differential Evolution

개선된 미분 진화 알고리즘에 의한 퍼지 모델의 설계

  • Received : 2012.04.20
  • Accepted : 2012.06.27
  • Published : 2012.08.25
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Abstract

Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.

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References

  1. R.M. Tong, "The evaluation of fuzzy models derived from experimental data," J . Fuzzy Sets and Syst., vol. 13, pp. 1-12, 1980.
  2. C.W. Xu, Y. Zailu, "Fuzzy model identification self learning for dynamic system," J. IEEE Trans. on System, Man, and Cybernetics, vol. 17, no. 4, pp. 683-689, 1987. https://doi.org/10.1109/TSMC.1987.289361
  3. S.K. Oh., W. Pedrycz, "Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems," J. Fuzzy Sets and Syst., vol. 115, no. 2, pp. 205-230, 2000. https://doi.org/10.1016/S0165-0114(98)00174-2
  4. S.K. Oh, W. Pedrycz, K.J Park, "Identification of fuzzy systems by means of genetic optimization and data granulation," Journal of Intelligent & Fuzzy Systems, vol 18, pp. 31-41, 2007.
  5. F. Herrera, "Genetic fuzzy stystems: taxonomy, current research trends, and prospects," Evol. Intel., vol 1, pp. 27-46, 2008. https://doi.org/10.1007/s12065-007-0001-5
  6. R. Storn, K. V. Price, "Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces," Global Optim., vol. 11, pp. 341-359, 1997. https://doi.org/10.1023/A:1008202821328
  7. G. Wenyin, C. Zhihua, J. Liangxiao, "Enhancing the performance of differential evolution using orthogonal design method," Applied Mathematics and Computation, vol. 206, pp. 56-69, 2008. https://doi.org/10.1016/j.amc.2008.08.053
  8. Q.Zhang, Y.W.Leung, "Orthogonal genetic algorithm for multimedia multicast routing," IEEE Transactions on Evolutionary Computation, vol. 3, pp. 53-62, 1999. https://doi.org/10.1109/4235.752920
  9. A.K. Qin, V.L.Huang, P.N. Suganthan. "Differential evolution algorithm with strategy adaptation for global numerical optimization," IEEE Transactions on Evolutionary Computation, vol. 13, pp. 398-417, 2009. https://doi.org/10.1109/TEVC.2008.927706
  10. W. Huang, L. Ding, S.K. Oh, C.W. Jeong, and S.C. Joo. "Identification of fuzzy inference system based on information granulation," KSII Transactions on Internet and Information systems, vol. 4, pp. 575-594, 2010.
  11. W. Huang, S.K. Oh, L. Ding, H.K. Kim, S.C. Joo, "Identification of fuzzy inference systems using a multi-objective space search algorithm and in formation granulation," Journal of Electrical Engineering & Technology, vol. 6, pp. 853-866, 2011. https://doi.org/10.5370/JEET.2011.6.6.853
  12. Pedrycz, W., and Kwak, K.C. "Linguistic models as a framework of user-centric system modeling," IEEE Trans. Syst., man cybern. - PART A: Systems and humans, vol. 36, no. 4, pp. 727-745, 2006. https://doi.org/10.1109/TSMCA.2005.855755
  13. Pedrycz, W., Park, H.S., and S.K. Oh, "A granular-oriented development of functional radial basis function neural networks," Neurocomputing, vol. 72, pp. 420-435, 2008. https://doi.org/10.1016/j.neucom.2007.12.016
  14. S. K. Oh, W. Pedrycz, and B. J. Park, "Relationbased Neurofuzzy Networks with Evolutionary Data Granulation," Mathematical and Computer Modeling, vol. 40, no. 7-8, pp. 891-921, 2004. https://doi.org/10.1016/j.mcm.2004.10.019
  15. S. K. Oh and W. Pedrycz, "Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks," Int. J . of General Systems, vol. 32, no. 3, pp. 237-250, 2003. https://doi.org/10.1080/0308107031000090756
  16. S. K. Oh, H. S. Park, C. W. Jeong, and S. C. Joo, "GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems," KSII Transactions on Internet and Information Systems, vol. 3, no. 3, pp. 309-330, Jun, 2009. https://doi.org/10.3837/tiis.2009.03.006
  17. W. Pedrycz and K. C. Kwak, "The Development of Incremental Models," IEEE Trans. Fuzzy Systems, vol. 15, no. 3, pp. 507-518, 2007. https://doi.org/10.1109/TFUZZ.2006.889967