Automatic generation of fuzzy rules using hyper-elliptic-cone membership functions by genetic algorithms

Article type: Research Article

Authors: Inoue, Hiroyuki | Kamei, Katsuari | Inoue, Kazuo

Affiliations: Department of Computer Science, Faculty of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi Kusatsu, Shiga 525-77, Japan. Tel.: +8 775 61 2807; Fax: +81 775 61 2669; E-mail: hiro@spice.cs.ritsumei.ac.jp, kamei@spice.cs.ritsumei.ac.jp, inoue@contlab.cs.ritsumei.ac.jp

Abstract: We had presented an automatic generation technique for fuzzy rules using 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 inherited in the next generation. In this paper, we expand the shape of fuzzy 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 the number of rules and obtains high-performance fuzzy rule sets. We applied presented methods to a line pursuit control problem and a trailer-truck back-up control problem. The method using hyper-elliptic-cone membership 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.

Journal: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 1, pp. 65-81, 1998