Journal of Automation and Information Sciences

ISSN for PRINT: 1064-2315

For Online Access

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2007, Volume39

Issue 8

80 pages

DOI: 10.1615/JAutomatInfScien.v39.i8

Issue price - $238.00

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.

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

Article price - $45.00

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