@article{(Open Science Index):https://publications.waset.org/pdf/5238, title = {An Adaptive Fuzzy Clustering Approach for the Network Management}, author = {Amal Elmzabi and Mostafa Bellafkih and Mohammed Ramdani}, country = {}, institution = {}, abstract = {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. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering principle to estimate both the number of clusters and the initial locations of a cluster centers. Each obtained cluster corresponds to a Mamdani fuzzy rule. Then, it optimizes the fuzzy model parameters by applying a genetic algorithm. This method is illustrated on a traffic network management application. We suggest also a Mamdani fuzzy rules generation method, where the expert wants to classify the output variables in some fuzzy predefined classes. }, journal = {International Journal of Computer and Information Engineering}, volume = {1}, number = {7}, year = {2007}, pages = {2068 - 2073}, ee = {https://publications.waset.org/pdf/5238}, url = {https://publications.waset.org/vol/7}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 7, 2007}, }