Fast generation method of fuzzy rules and its application to flux optimization in process of matter converting^①

来源期刊：中南大学学报(英文版)2006年第3期

论文作者：胡志坤 彭小奇 桂卫华

文章页码：251 - 255

Key words：fuzzy rule; data mining; Sugeno model; intelligent optimization; matter converting

Abstract: A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of two linguistic variables with the degree of certainty and the storage structure of rule base was described.The simulation results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5%.