A Hybrid Approach to Optimize Fuzzy If - Then Rules

In this paper we propose a hybrid approach attempting to find optimal value and points for profit and cost functions for which there is no equivalentequation . In this case only vague information as fuzzy ifthenrules are accessible. This approach involves five steps: In the first step, fuzzy rules by expert information arecollected and in the second step Mamdani inference method is applied to produce a surface called M-surface. As this surface is not a desirable one, so in the third step,data is extracted from the M-surface and they are used in Surrogate Software. Then in the forth step, by usingvarious methods in Surrogate more accurate vision is built for the mentioned function of fuzzy if-then rules. In the final step a Genetic Algorithm is applied to optimize theresult model. This approach can be used to optimize any unknown function via finding only a number of fuzzy ifthen rules of this function by the aid of experts

A Hybrid Approach to Optimize Fuzzy If - Then Rules Keywords:

Fuzzy rule , optimization , genetic algorithm , Surrogate modeling