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Fuzzy Model Identification Using VmGA

  • Park, Jong-Il (Department of Electronic and Information Engineering, Kunsan National University) ;
  • Oh, Jae-Heung (Department of Electronic and Information Engineering, Kunsan National University) ;
  • Joo, Young-Hoon (Department of Electronic and Information Engineering, Kunsan National University)
  • Published : 2002.03.01

Abstract

In the construction of successful fuzzy models for nonlinear systems, the identification of an optimal fuzzy model system is an important and difficult problem. Traditionally, sGA(simple genetic algorithm) has been used to identify structures and parameters of fuzzy model because it has the ability to search the optimal solution somewhat globally. But SGA optimization process may be the reason of the premature local convergence when the appearance of the superior individual at the population evolution. Therefore, in this paper we propose a new method that can yield a successful fuzzy model using VmGA(virus messy genetic algorithms). The proposed method not only can be the countermeasure of premature convergence through the local information changed in population, but also has more effective and adaptive structure with respect to using changeable length string. In order to demonstrate the superiority and generality of the fuzzy modeling using VmGA, we finally applied the proposed fuzzy modeling methodof a complex nonlinear system.

Keywords

References

  1. H. Takagi and Sugeno, 'Fuzzy Identification of Systems and Its Application to Modeling and Control', IEEE Trans. on Sys. Man and Cybern., vol. 15, pp. 116-132, 1985
  2. Y. H. Joo, H. S. Hwang, K. B. Kim and K. B. Woo,'Linguistic Model Identification for Fuzzy System', Electronics Letters, vol. 31, no. 4, pp. 330-331, 1995 https://doi.org/10.1049/el:19950163
  3. K. Shimojima, T. Fukuda and Y. Hasegawa, Self-Tuning Fuzzy Modeling with Adaptive Membership Function,Rules, and HierarchicaI Structure Based on Genetic Algo-rithm, Fuzzy Sets and Systems, vol. 71, pp. 295-309, 1995 https://doi.org/10.1016/0165-0114(94)00280-K
  4. D. E. Goldberg, Genetic Algorithms in Search, Optimization& Machine Learning, Addison-Wesley, 1990
  5. D. E. Goldberg, B. Korb, and K. Deb, 'Messy Genetic Algorithms: Motivation, Analysis, and First Results',Complex Systems, vol. 3, no. 5, pp. 493-530, 1989
  6. H. Kargupta, 'The Gene Expression Messy Genetic Algo-rithm', Proc. of IEEE Int. Conf. on Evolutionary Com-putation, Nagoya, Japan, 1996
  7. F. Hoffmann and G. Pfister, 'A New Learning Methodfor the Design of Hierarchical Fuzzy Controllers UsingMessy Genetic Algorithms', Proc. IFSA '95, July 1995
  8. M. Chowdhury and Y. Li, 'Messy Genetic AlgorithmBased New Learning Method for Structurally Optimized Neurofuzzy Controllers', Proc. IEEE Int. Conf. on Indu-strial Tech., Dec. 1996
  9. N. Kubota, T. Fukuda and K.. shimojima, 'Virus-Evolu-tionary Genetic Algorithm for a Self- Organizing Manufacturing System', Computers & Industrial Engineering, vol. 30, Issue 4, pp. 1015-1026, Sep. 1996 https://doi.org/10.1016/0360-8352(96)00049-6
  10. N. Kubota, K. shimojima, T. Fukuda, 'The Role of Virus Infection in Virus-Evolutionary Genetic Algorithm', Proc. of IEEE Int. Conf. on, pp. 182-187, 1996