Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지

Fuzzy Modeling Using Virus-Evolutionary Genetic Algorithm

바이러스-진화 유전 알고리즘을 이용한 퍼지 모델링

  • 이승준 (연세대 전기·컴퓨터공학과) ;
  • 주영훈 (군산대 전자정보공학부) ;
  • 박진배 (연세대 전기·컴퓨터공학과)
  • Published : 2000.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Genetic algorithm has been used to identifY parameters and structure of fuzzy model because it has the ability to search optimal solution somewhat globally. The genetic algorithm, however, has a problem, which optimization process can be premature convergence in the case of lack of genetic divergence of population. Virus- evolutionary genetic algorithm(VEGA) could be a strategy against this local convergence. Therefore, we use VEGA for fuzzy modeling. In this method, local information is exchanged in population so that population can sustain genetic divergence. finally, to prove the theoretical hypothesis, we provide numerical examples to evaluate the feasibility and generality of fuzzy modeling using VEGA.

본 논문은 기존의 수학적인 모델링으로는 만족스러운 결과를 얻기 어려운 복잡하고 불확실한 비선형 시스템에 대한 퍼지 모델링 기법을 다룬다. 유전 알고리듬은 어느 정도 최적해를 전역적으로 찾을 수 있기 때문에 퍼지 모델링시에 파라미커와 구조를 동정하기 위하여 사용되었다. 하지만, 유전 알고리듬은 개체군이 유전적 다양성을 잃었을 경우 조기 수렴한다는 문제점이 있으며 바이러스-진화 유전 알고리듬은 이러한 지역수렴에 대한 방아닝 될 수 있다. 따라서, 본 논문에서는 바이러스 이론이 적용된 VEGA를 퍼지 모델링 할 때 이용할 수 있는 방법을 제안한다. 이 방법에서는 지역정보가 개체군 내에서 교환됨으로써 유전적 다양성을 유지하게 된다. 마지막으로, 본 논문에서 제안한 방법의 우수성과 일반성을 평가하기 위해 몇 가지의 수치적 예제를 제공한다.

Keywords

References

  1. Fuzzy Sets and Systems v.4 The Evaluation of Fuzzy Models Derived from Experimental Data R.M. Tong
  2. Fuzzy Sets and Systems v.13 An Identification Algorithm in Fuzzy Relational Systems W. Pedrycz
  3. IEEE Trans. Syst. Man. Cybern. v.17 no.4 Fuzzy Model Identification and Self-learning for Dynamic Systems C.W. Xu
  4. IEEE Trans. on Sys. Man and Cybern. v.15 Fuzzy Identification of Systems and Its Application to Modeling and Control H. Takagi;M. Sugeno
  5. Fuzzy Sets and Systems v.28 Structure Identification of Fuzzy Model M. Sugeno;G.T. Kang
  6. IEEE Trans. on Fuzzy System v.1 no.1 A Fuzzy-Logic-Based Approach to Qualitative Modeling M. Sugeno;T. Yasukawa
  7. Information Control v.8 Fuzzy Sets L.A. Zadeh
  8. Fuzzy Sets and Systems v.86 no.3 Fuzzy System Modeling by Fuzzy Partition and GA Hybrid Schemes Y.H. Joo.;H.S. Hwang;K.B. Kim;K.B. Woo
  9. Proc. of The Fourth Int. Conf. on Genetic Algorithms A Natural Occurring Niche & Species Phenomenon : The Model and First Results Y. Davidor
  10. Proc. of The Fourth Int. Conf. on Genetic Algorithm Fine-Grained Parallel Genetic Algorithms B. Manderick;P. Spoessens
  11. A Genetic Algorithm for Parallel Simulated Annealing, Parallel Problem Solving from Nature 2 S. Mahfoud;D.E. Golderg
  12. Proc. of the First IEEE Conf. on Evolutionary Computing v.1 Hybridizing Genetic Algorithms with Hill-Climbing Methods for Global Optimization : Two Possible Ways J. Renders;H. Bersini
  13. Computers & Indestrial Engineering v.30 Virus-Evolutionary Genetic Algorithm for a Self-Organizing Manufacturing System N. Kubota;T. Fukuda;K. Shimojima
  14. Genetic Algorithms in Search, Optimization and Machine Learning J.H. Holland
  15. Ecology : Individuals, Populations and Communities M. Begon;J.L. Harper;C.R. Townsend
  16. Proc. of IEEE Int. Conf. on The Role of Virus Infection in Virus- Evolutionary Genetic Algorithm N. Kubota;K. Shimojima;T. Fukuda
  17. 대한 전기학회 논문집 v.47 no.6 비선형 시스템의 퍼지 모델링에 관한 연구 장욱;손유석;주영훈;박진배
  18. Time Series Analysis, Forecasting and Control G.E.P. Box;G.M. Jenkins
  19. Electronics Letters v.31 no.4 Linguistic Model Identification for Fuzzy System Y.H. Joo;H.S. Hwang;K.B. Kim;K.B. Woo