Applying bacterial algorithm to optimise trapezoidal membership functions in a fuzzy rule base
نویسندگان
چکیده
This paper presents a method of using the so-called „bacterial algorithm” [4, 5] for extracting the fuzzy rule base from a training set. The class of membership functions is restricted to trapezoidal, as it is general enough and widely used. The pseudobacterial genetic algorithm (PBGA) is show. The PBGA optimises the trapezoidal membership functions in the rules by the bacterial mutation operator. This allows the change of more than one membership function at one time, and fine-tuning as well. Besides, it is important to determine the optimal rule number in the rule base. For this, further operators are used, which eliminates the ineffective rules and contract groups of two or more similar rules into single ones.The evaluation criteria of these operators for trapezoidal membership functions is proposed. ...
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