Inverse Problem Solving based on IF-THEN Rules and Genetic Algorithms

Alexander P. Rotshtein, Hanna B. Rakytyanska.

This paper proposes an approach for inverse problem solving based on the description of the interconnection between unobserved and observed parameters of an object with the help of fuzzy IF-THEN rules. The essence of the approach proposed consists in formulating and solving the optim-ization problems, which, on the one hand, find the roots of fuzzy logical equations, corresponding to IF-THEN rules, and on the other hand, tune the fuzzy model on the readily available experimental data. The genetic algorithms are proposed for the optimization problems solving.

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