한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제14권1호
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  • Pages.46-51
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  • 2004
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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선형 행렬 부등식을 이용한 TS 퍼지 분류기 설계

TS Fuzzy Classifier Using A Linear Matrix Inequality

  • 김문환 (연세대학교 전기전자공학과) ;
  • 주영훈 (군산대학교 전자정보공학부) ;
  • 박진배 (연세대학교 전기전자공학과)
  • 발행 : 2004.02.01
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 선형행렬 부등식을 이용한 TS 퍼지 분류기 설계 방법을 제안한다. TS 퍼지 분류기를 설계하기 위해 퍼지규칙의 후반부 파라메터가 분류기의 성능을 최대로 하도록 동정되어야 한다. 이러한 동정 문제를 해결하기 위해 볼록 최적화 기법이 사용되었다. 후반부 파라메터 동정 문제는 볼록 최적화 문제로 변환되며, 선형행렬 부등식으로 표현된다. 선형행렬 부등식으로 표현된 볼록 최적화 문제는 일반 고유값 문제로 근사화 되며, 일반 고유값 문제를 최적화함으로써 최소의 분류 에러를 가지는 최적의 후반부 파라메터가 결정된다. 제안된 분류기의 성능을 평가하기 위해 IRIS 데이터와 Wisconsin Breast Cancer Database 데이터에 대한 분류기의 성능을 모의 실험을 통해 확인하였다. 마지막으로, 모의 실험 결과 제안된 TS 퍼지 분류기의 성능의 우수성을 확인할 수 있었다.

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.

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참고문헌

  1. L. A. Zadeh, "Fuzzy sets, information and control", Vol. 8, Page(s): 338-353, 1965. https://doi.org/10.1016/S0019-9958(65)90241-X
  2. P. K. Simpson, "Fuzzy min-max neural networks. I. Classification", IEEE Trans. on Neural Networks, Vol. 3, pp. 776-786, 1992. https://doi.org/10.1109/72.159066
  3. M. Setnes and H. Roubos, "GA-fuzzy modeling and classification: complexity and performance", IEEE Trans. on Fuzzy Systems, Vol. 8, pp. 509 -522, 2000. https://doi.org/10.1109/91.873575
  4. S. Abe and M.S .Lan, "A method for fuzzy rules extraction directly from numerical data and its application to pattern classification", IEEE Trans. on Fuzzy Systems, Vol. 3, pp. 1 -28, 1995. https://doi.org/10.1109/91.366566
  5. S. Abe and R. Thawonmas, "A fuzzy classifier with ellipsoidal regions", IEEE Trans. on Fuzzy Systems, Vol. 5, pp. 358-368, 1997. https://doi.org/10.1109/91.618273
  6. S. Abe, R. Thawonmas, and M. Kayama, "A fuzzy classifier with ellipsoidal regions for diagnosis problems", IEEE Trans. on Systems, Man and Cybernetics, Vol. 29, pp. 140-148, 1999. https://doi.org/10.1109/5326.740676
  7. M. Sugeno and G. T. Kang, "Structure identification of fuzzy model", Fuzzy Sets and Systems. Vol. 28, pp. 15-33, 1988. https://doi.org/10.1016/0165-0114(88)90113-3
  8. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control", IEEE Trans. on Systems, Man and Cybernetics, Vol. 15, pp. 116-132, 1985.
  9. Y. Nesterov and A. Nemirovsky, Interior-point polynomial methods in convex programming, SIAM. Philadelphia, PA, 1994.
  10. O. Cordon, M. J. Jesus. and F. Herera, "A proposal on reasoning methods in fuzzy rule-based classification systems", Int. Journal of Approximate Reasoning, Vol. 20, pp. 21-45, 1999. https://doi.org/10.1016/S0888-613X(00)88942-2
  11. L. I. Kuncheva, "How good are fuzzy If-Then classifiers?", IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol. 30, pp. 501-509, 2000. https://doi.org/10.1109/3477.865167
  12. M. Setnes and H. Roubos, "GA-fuzzy modeling and classification: complexity and performance ", IEEE Trans. on Fuzzy Systems, Vol. 8, pp. 509 -522,2000. https://doi.org/10.1109/91.873575
  13. J. Yen, L, Wang, and C. W. Gillespie, "Improving the inter ability of TSK fuzzy models by combining learning and local learning", IEEE Trans. on Fuzzy Systems, Vol. 6, pp. 530-537, 1998. https://doi.org/10.1109/91.728447
  14. R. Li, M. Mukaidono and I.B. Turksen, "A fuzzy neural network for pattern classification and feature selection", Fuzzy Sets and Systems, Vol. 130, pp. 101-140, 2002. https://doi.org/10.1016/S0165-0114(02)00050-7
  15. T.P. Hong and J. B. Chen, "Processing individual fuzzy attributes for fuzzy rule induction", Fuzzy Sets and Systems, Vol. 112, pp. 127-140, 2000. https://doi.org/10.1016/S0165-0114(98)00179-1
  16. H. M. Lee, C. M. Chen, J. M. Chen, and Y. L. Jou, "An efficient fuzzy classifier with feature selection based on fuzzy entropy", IEEE Trans. on Systems. Man and Cybernetics, Part B, Vol. 31, pp. 426-432, 200l. https://doi.org/10.1109/3477.931536
  17. H. M. Lee, C. M. Chen, and Y. F. Lu, "A self -organizing HCMAC neural network classifier ", IEEE Trans. on Neural Network, Vol. 14, pp. 15-27, 2003. https://doi.org/10.1109/TNN.2002.806607
  18. M. Muselli and D. Liberati, "Binary rule generation via hamming clustering", IEEE Trans. on Knowledge and Data Engineering, Vol. 14, pp. 1258-1268, 2002.