DOI QR코드

DOI QR Code

Fuzzy Rule Generation and Optimization for the Intelligent Diagnosis System of Diseases associated with Acute Abdominal Pain Based on Fuzzy Relational Products

급성복통과 관련된 지능형 질환 진단시스템을 위한 퍼지 규칙 생성과 이의 최적화

  • 현우석 (한국성서대학교 정보과학부)
  • Published : 2004.12.01

Abstract

This paper describes knowledge base optimization of an intelligent diagnosis system based on fuzzy relational products(IDS-DAAP) for the diseases with acute abdominal Pain. The knowledge base of IDS-DAAP is composed of the fuzzy rules and the fuzzy membership functions. The author here proposes an advanced intelligent diagnosis system (A-lDS-DAAP) in which the fuzzy rule generation algorithm is applied. Comparing with previous IDS-DAAP and IDS-DAAP-NN, a modified approach with A-IDS-DAAP shows that it improves the diagnosis rate and reduces the time to diagnose.

본 논문에서는 급성복통과 관련된 지능형 질환 진단시스템에서 지식베이스의 최적화에 대해서 논한다. 급성복통과 관련된 지능형 질환 진단시스템의 지식베이스는 퍼지 규칙과 퍼지 멤버쉽 함수들로 구성되는데, 본 연구에서는 효율적으로 퍼지 규칙을 생성하는 알고리즘을 적용한 개선된 급성복통과 관련된 지능형 질환 진단 시스템(A-lDS-DAAP)을 제안한다. 제안하는 시스템은 기존의 IDS-DAAP, IDS-DAAP-NN과 비교해 볼 때, 진단의 정확성을 높이면서 수행속도를 향상시켰다.

Keywords

References

  1. L. J. Khout, E. Keravnou and W. Bandler, 'Automatic documentary information retrieval by means of fuzzy relational products,' In Gaines, B. R., Zadeh, L. A. and Zimmermann, H. J., editors Fuzzy Sets in Decision Analysis, North-Holland, Amsterdam, pp.308-404, 1984
  2. W. Bandler and L. J. Kohout, 'Fuzzy Relational Products as a Tool for Analysis and Synthesis of the Behaviour of Complex natural and Artificial System,' in: S. K, Wang and P. P. Chang, eds., Fuzzy Sets : Theory and Application to Analysis and Information Systems, Plenum Press, New York, pp.341-367, 1980
  3. W. Bandler and L. J. Kohout, 'Semantics of Implication Operators and Fuzzy Relational Products,' Intl. Journal of Man-Machine Studies, Vol.12, pp.89-116, 1980 https://doi.org/10.1016/S0020-7373(80)80055-1
  4. W. Bandler and L. J. Kohout, 'Fuzzy Power Sets and Fuzzy Implication Operator,' Fuzzy Set and Systems 4, pp.13-30, 1980 https://doi.org/10.1016/0165-0114(80)90060-3
  5. 현우석, '퍼지관계곱 기반 급성복통과 관련된 지능형 질환 진단시스템의 설계 및 구현', 정보처리학회논문지B, 제10-B권 제2호, pp.197-204, 2003
  6. M. Ayoubi, 'Neuro-fuzzy structure for rule generation and application in the fault diagnois of technical processes,' Proc. of American Control Conference, Seattle, pp.2757-2761, 1995
  7. F. C.-H. Rhee and R. Krishnapuram, 'Fuzzy rule generation methods for high-level computer vision,' Fuzzy Sets and Systems, Vol.60, pp.245-258, 1993 https://doi.org/10.1016/0165-0114(93)90436-L
  8. S. Mirta and S. K. Pal, 'Fuzzy multi-layer perceptron, inference and rule generation,' IEEE Trans., Neural Networks, Vol.6, pp.51-63, 1995 https://doi.org/10.1109/72.363450
  9. E. Tazaki and N. Inoue, 'A generation method for fuzzy rules using neural networks with Planar Lattice architecture,' Proc. of IEEE Int. Conf, Neural Networks, pp.1743-1748, 1994 https://doi.org/10.1109/ICNN.1994.374419
  10. Shiqian Wu, Meng Joo Er and Yang Gao, 'A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks,' IEEE Transactions on Fuzzy Systems, Vol.9, No.4, 2001 https://doi.org/10.1109/91.940970
  11. T. W. Cheng, D. B. Goldgof and L. O. Hall, 'Fast clustering with application to fuzzy rule generation,' Proc. of IEEE Int. Conf. Fuzzy Syst., pp.2289-2295, 1995 https://doi.org/10.1109/FUZZY.1995.409998
  12. Yi Lu and Tie Qi Chen, 'Fast Rule Generation and Membership Function Optimization for a Fuzzy Diagnosis System,' The Tenth International conference on Industrial & Engineering Applications of Artificial Intelligence Expert Systems, June, 1997
  13. 김성학, ' Gentic 알고리즘을 이용한 풀 온도 제어 시스템의 지식베이스 최적화', 정보처리학회논문지, 제1권 제3호, pp.319-326, 1994
  14. 이종우, 김유섭, 김성동, 이재원, 채진석, '패턴 매칭과 자동 규칙 생성에 기반한 2단계 주식 트레이딩 시스템', 정보처리학회논문지B, 제10-B권 제3호, pp.257-264, 2003