Extraction of Fuzzy Knowledge Bases from Experimental Data by Genetic Algorithms

  • Authors:
  • A. P. Rotshtein;Yu. I. Mityushkin

  • Affiliations:
  • Polytechnic Institute Makhon Lev, Jerusalem, Israel;Vinnitsa State Technical University, Ministry of Education of Ukraine, Vinnitsa, Ukraine

  • Venue:
  • Cybernetics and Systems Analysis
  • Year:
  • 2001

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Abstract

This paper deals with the problem of determination of linguistic "IF-THEN" rules from available experimental data, which is inverse to the problem of identification of nonlinear dependences by fuzzy knowledge bases. A method of genetic algorithms is proposed. The method is based on the operations of crossover, mutation, and selection of initial variants of solutions or so-called chromosomes, from which the most optimal solutions are subsequently chosen. The method is illustrated by a computer experiment consisting of the determination of knowledge on a nonlinear object with two input variables and one output variable.