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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN On-line: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v39.i8.50
pages 39-52

Ensuring Accuracy and Transparency of Mamdani Fuzzy Model in Learning by Experimental Data

Sergey D. Shtovba
Vinnitsa National Technical University, Ukraine


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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