每年出版 12 期
ISSN 打印: 1064-2315
ISSN 在线: 2163-9337
Indexed in
Ensuring Accuracy and Transparency of Mamdani Fuzzy Model in Learning by Experimental Data
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摘要
Typical violations of transparency of the Mamdani fuzzy model, which arise as a side effect of learning by experimental data are revealed. We suggest a new learning scheme of the Mamdani fuzzy model, which differs from the known ones by the following: 1) expansion of bearers of fuzzy sets of output variables; 2) excluding coordinates of maxima of belonging functions of extreme terms from the list of parameters to be tuned; 3) introducing constraints for linear ordering of fuzzy sets within limits of one term-set. Computer simulations indicate that learning by the new scheme does not break transparency of a fuzzy model. Moreover, accuracy of fuzzy model is not worse than for the case of typical learning.
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