A Study of the Optimization for Fuzzy Diagnostic Rules Based on the Reformative CHC Algorithm
Yi Qi · Acta Aeronautica Et Astronautica Sinica · 2004
In the machine learning of rules for the diagnostic expert systems, a mass of fuzzy rules have to be optimized one time. It makes the optimization algorithm run much time that can't accord with the need in practice. In order to solve this problem, a new genetic arithmetic operator-best fraction recombination operator is proposed, and the CHC algorithm is reformed with it, which is used in the optimization algorithm in this paper. The simulation shows that the reformative CHC algorithm has much advantage in the optimization for fuzzy rules because of getting the satisfying result in a short consumed time. It can solve the optimization of fuzzy diagnostic rules while the ordinary genetic algorithm cannot.