Evolutionary Strategies for Fuzzy Models: Local vs Global Construction

Document Type

Article

Publication Date

1999

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Abstract

This paper presents a framework for studying the effectiveness of evolutionary strategies for generating fuzzy rule bases from training data. The fitness measure needed for selection is obtained by a comparison of the training data with the function approximation defined by a fuzzy rule base. The properties of employing both global and local fitness measures are examined. Rule base completion is obtained by incorporating a global evaluation of the smoothness of the transitions between local regions into the selection process.

Comments

Presented at the 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS, 1999, New York, NY.

DOI

10.1109/NAFIPS.1999.781683

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