AUTOMATIC-GENERATION OF FUZZY RULES USING HYPER-ELLIPTIC-CONE MEMBERSHIP FUNCTIONS BY GENETIC ALGORITHMS

We had presented an automatic generation technique for fuzzy rules usi ng hyper-cone membership functions by genetic algorithms (GA). However , there remain some problems. The shape of fuzzy subsets is limited to a hyper-sphere. Since there was not a regulation which determines the order of rules, two rules having different antecedent part structure are crossed in Crossover, and high-performance rules may not be inheri ted in the next generation. In this paper, we expand the shape of fuzz y subsets to be elliptic and present an automatic generation technique for fuzzy rules using hyper-elliptic-cone membership functions by GA. We also present a rules sorting technique which efficiently reduces t he number of rules and obtains highperformance fuzzy rule sets. We app lied presented methods to a line pursuit control problem and a trailer -truck back-up control problem. The method using hyper-elliptic-cone m embership functions can obtain very accurate fuzzy system with as high performance as the method using hyper-cone membership functions. The technique of a sort of rules can not only reduce the number of rules, but also can get a high-performance rule set.