AUTOMATIC GENERATION OF FUZZY INFERENCE SYSTEMS USING HEURISTIC POSSIBILISTIC CLUSTERING

Abstract:1. Introduction Someremarksonfuzzyinferencesystemsareconside-red in the first subsection. The second subsection inclu-des a brief review of methods of extracting of fuzzy rulesbasedonfuzzyclusteringandtheaimsofthepaper.Fuzzy inference systems are one of the most famousapplications of fuzzy logic and fuzzy sets theory. Theycan be helpful to achieve classification tasks, processsimulation and diagnosis, online decision support toolsand process control. So, the problem of generation offuzzyrulesisoneofmorethanimportantproblemsinthedevelopmentoffuzzyinferencesystems.There are a number of approaches to learning fuzzyrules from data based on techniques of evolutionary orneural computation, mostly aiming at optimizing para-meters of fuzzy rules. From other hand, fuzzy clusteringseems to be a very appealing method for learning fuzzyrulessincethereisacloseandcanonicalconnectionbet-ween fuzzy clusters and fuzzy rules. The idea of derivingfuzzyclassificationrulesfromthedatacanbeformulatedas follows: the training data set is divided into homoge-neousgroupandafuzzyruleisassociatedtoeachgroup.Fuzzy clustering procedures are exactly pursuing thestrategy:afuzzyclusterisrepresentedbytheclustercen-ter and the membership degree of a datum to the clusteris decreasing with increasing distance to the clustercenter.So, each fuzzy rule from a fuzzy inference system canbecharacterizedbyatypicalpointandmembershipfunc-