International Journal of Fuzzy Logic and Intelligent Systems

  • 제5권1호
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  • Pages.7-12
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  • 2005
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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A Study of Construct Fuzzy Inference Network using Neural Logic Network

  • Lee, Jae-Deuk (Dept. Of Automobile Engineering, Chosun Collage of Science & Technology) ;
  • Jeong, Hye-Jin (School of Electronics & Information Engineering, ChonBuk National University) ;
  • Kim, Hee-Suk (Dept. Of Multimedia, Asan IT Polytechnic Collage) ;
  • Lee, Malrey (School of Electronics & Information Engineering, ChonBuk National University)
  • 발행 : 2005.03.01
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초록

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. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.

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참고문헌

  1. B. T. Low, H.C. Lui, A. H. Tan, and H. H. Teh, 'Connectionist Expert System with Adaptive Learning Capability,' IEEE Transaction on Knowledge and Data Engineering, Vol. 13, No.2, June, (2001). pp. 200-207
  2. L. S. Hsu, H. H. The, S. C. Chan and K. F. Loe, 'Fuzzy Logic in Connectionist Expert Systems', IJCNN'2000, Vol. 2, (2000). pp.599-602
  3. Tatsuki Watanabe, Masayuki Matsumoto and Takahiro Hasegawa, 'A Layered Neural Network Model using Logic Neurons,' in Proc. Of the International Conf. On Fuzzy Logic & Neural Networks, (2000). pp.675-678
  4. Wang-Pei Zhuang, Wu Zhi Qiao and The Hoon Heng, 'The Truth-Valued Flow Inference Network,' in Proc. Of the International Conf. On Fuzzy Logic & Neural Networks, (2000).pp.267 -281
  5. Stephen I. Gallant, 'Connectionist Expert Systems,' Comm. Of the ACM, Vol.39, pp. 152-169, (2000)
  6. Ricardo Jose Machado and Armando Freitas da Rocha, 'Fuzzy connectionist Expert System', in Proc. of IEEE International Conf. on Neural Networks, Vol. 7, pp. 1571-1576, (1998) https://doi.org/10.1109/ICNN.1994.374390
  7. Elie Sanchez, 'Fuzzy Connectionist Expert System,' in Proc. of the International Conf. on Fuzzy Logic & Neural Networks, (1999).pp.31-35
  8. Stephen I. Gallant, 'Neural Network Learning and Expert System,' The MIT press, (1993). pp.255-293
  9. Shashi Shekhar and Minesh B. Amin, 'Generalization by Neural Networks,' IEEE Transactions on Knowledge and Data Engineering, Vol. 10, No., 2, pp. 28-38, (2000).pp.28-38
  10. Adam Blum, 'Neural Networks in C++,' Wiley, (1992)
  11. K. Hirota and W. Pedrycz, 'Fuzzy Logic Neural Networks:Design and Computations,' IJCNN, (2001). pp.152-157
  12. Zadeh L. A., 'The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems', Fuzzy Sets and Systems, Vol. 21, (1993), pp. 199-227
  13. Hisao ISHIBUCHI, and Hideo TANAKA,' Interpolation of Fuzzy If-Then Rules by Neural Networks,' in Proc. Of the second International Conf. On Fuzzy Logic & Neural Networks, 1998, pp.337-340