A RESEARCH ON WEIGHT ACQUISITION OF WEIGHTED FUZZY PRODUCTION RULES BASED ON GENETIC ALGORITHM
In order to increase the knowledge representation power and improve generalization capability of Fuzzy Production Rules (FPRs), a weighted FPRs(WFPRs), which incorporates the concepts of knowledge representation parameters(local weight, global weight and threshold), has been presented.However, the acquisition of these knowledge representation parameters is significant but difficult. This paper advances the weights acquisition of WFPRs by means of Genetic Algorithm(GA). First, a model based on GA is designed to obtain the local weights and the global weights of WFPRs; and then, the experiments on classification problem with continuous-valued attributes is performed. The experiments proved that the generalization capability of a fuzzy production system can be improved greatly by training and optimizing the weights with the use of GA, and verified the rationality and validity of the method sequentially.
Weighted Fuzzy Production Rules Local weight Global weight Genetic Algorithm
MIAO WANG XI-ZHAO WANG
Dept.of Mathematics and Computer Science, Hebei University, Baoding 071002,China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2208-2211
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）