Comparative analysis of two fuzzy rule base optimization methods
Zsolt Csaba Johanyák, Olga Papp · 2011
Rule base optimization is a key step in fuzzy model identification that determines the performance of the fuzzy system. In this paper, we examine two optimization solutions, i.e. the cross-entropy method and a hill climbing approach based heuristic method. They are used and compared in case of four benchmarking problems. In each case the initial rule base is created by a fuzzy clustering based method.