한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제14권6호
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  • Pages.776-782
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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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Development of a New Max-Min Compositional Rule of Inference in Control Systems

  • Cho, Young-Im (Dept. of Computer Science, Pyongtaek University)
  • Published : 2004.10.01
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Abstract

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

Keywords

References

  1. L.A.Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Mad, and Cybernetics 3(1973) 28-44 https://doi.org/10.1109/TSMC.1973.5408575
  2. D.Dubois, H.Prade, The Generalized Modus Ponens under Sup-Min Composition – A Theoretical Study -, Approximate Reasoning in Expert Systems, in: M.M.Gupta, A Kandel, W.Bandler, J.B.Kiszka (Ed.), Elservier Publishers B.V. (North-Holland), 1985 217-232.
  3. J.B.Kiszka, M.E.Kochanska, D.S.Sliwinska, The Influence of Some Implication Operators on the Accuracy of a Fuzzy Model-Part I, Fuzzy Sets and Systems 15 (1985) 112-128.
  4. Z.Cao, A.Kandel, Applicability of Some Fuzzy Implication Operators, Fuzzy Sets and Systems 31 (1989) 151-186. https://doi.org/10.1016/0165-0114(89)90002-X
  5. Y.Takefuji, Meng-hiot Lim, Computation Scheme for the General Purpose VLSI Fuzzy Inference Engine as Expert System, Butterworth and Co(publishers) Ltd. 2:2(1989) 109-116. https://doi.org/10.1016/0950-7051(89)90014-2
  6. M.B.Gorzalczany, On some idea of a neuro-fuzzy controller, Information Sciences 120(1999) 69-87. https://doi.org/10.1016/S0020-0255(99)00069-9
  7. K.Kasabov.N., On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks, Neurocomputing 41(2001), 25-45. https://doi.org/10.1016/S0925-2312(00)00346-5
  8. W.S.Lin, C.H.Tsai. J.S.Liu, Robust neuro-fuzzy control of multivariable systems by tuning consequent membership functions, Fuzzy Sets and Systems 124(2001) 180-195.
  9. Y.Wang, G.Rong, A self-organizing neural-network- based fuzzy system, Fuzzy Sets and Systems 103(1999) 1-12. https://doi.org/10.1016/S0165-0114(97)00196-6
  10. M.I.Sessa, Approximate reasoning by similarity-based SLD resolution, Theoretical Computer Science 275 (2002) 389-426. https://doi.org/10.1016/S0304-3975(01)00188-8
  11. .B.Turksen, Z.Zhong, An Approximate Analogical Reasoning Approach Based on Similarity Measures, IEEE Transaction on SMC, 18(1988) 1049-1056. https://doi.org/10.1109/21.23107