A Conditional Fuzzy Clustering with Adaptive Method
نویسندگان
چکیده
The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. Those rules are not explicit for the expert. This paper proposes a new method to generate Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps. The first step consists in using the subtractive clustering principle to estimate the number of clusters and the initial locations of cluster centers, each obtained cluster corresponds to a Mamdani fuzzy rule. The second step optimizes the fuzzy model parameters by using a genetic algorithm. This method has been implemented and tested in the framework of a traffic network management application. This method has been extended to generation of Mamdani fuzzy rules when fuzzy classes can be predefined by the expert.
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