Abstract
Combining fuzzy rule interpolation with the use of hierarchically structured fuzzy rule bases, as proposed by Sugeno leads to the reduction of the fuzzy algorithms’ complexity. In this paper mainly the KH method and its versions are used for interpolation. One of the drawbacks of this method is that it often results in abnormal conclusions, so the hierarchical structures are impossible to use. This paper describes how this difficulty can be avoided by using a modified version of the KH method, the MACI algorithm.
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This research is supported by the Hungarian Ministry of Culture and Education (MKM) FKFP 0235/1997 and FKFP 0422/1997, and the National Science Research Fund (OTKA) T019671 and T030655, and the Australian Research Council, and the Australian Research Council.
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References
P. Baranyi, D. Tikk, Yeung Yam and L.T. Kóczy: “Investigation of a new alpha-cut Based Fuzzy Interpolation Method”, Tech. Rep., Dept. Of Mechanical and Automation Engineering, The Chinese University of Hong Kong, 1999
S. Kawase and Q. Chen: “On fuzzy reasoning by Kóczy’s Linear Rule Interpolation”, Tech.Rep,. Teikyo Heisei University, Ichihara, Chiba, Japan, 1996, 9p.
L.T. Kóczy and K. Hirota: “Ordering, distance and closeness of fuzzy sets”, Fuzzy Sets and Systems 59 1993, pp. 281–293
L.T. Kóczy and K. Hirota: “Approximate reasoning by linear rule interpolation and general approximation”, Internat. J. of Approximate Reasoning, 9, 1993, pp. 197–225
L.T. Kóczy and K. Hirota: “Interpolative reasoning with insufficient evidence in sparse fuzzy rule bases”, Information Sciences 71 1993, pp. 169–201
L.T. Kóczy and K. Hirota: “Interpolation in structured fuzzy rule bases”, FUZZ-IEEE’93, San Francisco 1993, pp. 803–808
D. Tikk, P. Baranyi: “Comprehensive analysis of a new fuzzy rule interpolation method”, IEEE Trans. on Fuzzy Systems, 8 (3), 2000, pp.281–296
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© 2002 Springer-Verlag Berlin Heidelberg
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Kóczy, L.T., Muresan, L. (2002). Interpolation in Hierarchical Rule-Bases with Normal Conclusions. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_5
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DOI: https://doi.org/10.1007/3-540-45631-7_5
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