<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/5238" mdate="2007-07-24 00:00:00"> <author>Amal Elmzabi and Mostafa Bellafkih and Mohammed Ramdani</author> <title>An Adaptive Fuzzy Clustering Approach for the Network Management</title> <pages>2068 - 2073</pages> <year>2007</year> <volume>1</volume> <number>7</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/5238</ee> <url>https://publications.waset.org/vol/7</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>The Chius method which generates a TakagiSugeno 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. </abstract> <index>Open Science Index 7, 2007</index> </article>