Fast generation method of fuzzy rules and its application to flux optimization in process of 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%.

Keywords

fuzzy rule / data mining / Sugeno model / intelligent optimization / matter converting

References

[1] YinA-dong, GaoXue-dong, WuShen, et al. . Ming algorithm of association rules based on number data [J]. Microcomputer Development, 2003, 13(4): 67-70(in Chinese)

[2] ZhangZhao-hui, LuYu-chang, ZhangBo. An effective partitioning rules[C]. Proceedings of Pattern Acquisition and Knowledge Discovery of Database(PA-KDD), 1997, Singapore, World Scientific Publishing Co: 241251

[3] HuZhi-kun, GuiWei-hua, PengXiao-qi. Data mining in nonferrous metallurgical process[J]. Nonferrous Metals, 2003, 55(2): 40-12(in Chinese)

[4] SushmitaM, SankarK P, PabitraM. Data mining in soft computing framework: A survey [J]. IEEE Transactions on Neural Networks, 2002, 13(1): 3-14

[5] ChenNing, ChenAn, ZhouLong-xiang. Efficient algorithms for mining fuzzy rules in large relational databases[J]. Journal of Software, 2001, 12(7): 949-959

[6] ChenS M, HsiaoW H. Bidifectional approximate reasoning for rule-based systems using interval-valued fuzzy sets[J]. Fuzzy Sets and Systems, 2000, 113: 185-203

[7] CaiZi-xingArtificial intelligent and its application [M], 2002, Changsha, Central South University of Technology Press(in Chinese)

[8] ZhangZhi-xingNeural-fuzzy and soft computing [M], 1996, Xi’an, Xian Jiaotong University Press(in Chinese)

[9] KarrC L, GentryE J. Fuzzy control of pH using genetic algorithms[J]. IEEE Trans Fuzzy System, 1993, 1(1): 46-53

[10] FranceC, RichardL. Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm[J]. IEEE Transaction on Systems, Man and Cybernetics (Part B), 2000, 30(1): 31-45

[11] SugenoM, KangG T. Structure identification of fuzzy model[J]. Fuzzy Sets and Systems, 1988, 28(1): 15-33

[12] TakagiT, SugenoM. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transaction on Systems, Man and Cybernetics (Part B), 1985, 15(1): 116-132

[13] YinZhao-qing, YinHaoArtificial intelligence and expert system[M], 2001, Beijing, Chinese Water Conservancy and Water Electricity Press(in Chinese)

[14] JangS R. ANFIS: adaptive-network-based fuzzy inference system[J]. IEEE Transaction on Systems, Man and Cybernetics (Part B), 1993, 23: 665-668

[15] KenN, HisaoI, HideoT. A simple but powerful heuristic method for generating fuzzy rules from numerical data[J]. Fuzzy Sets and Systems, 1997, 86: 251-270

[16] RenHong-jiu, HuJun, HuZhi-kunNonferrous metallurgic bath smelting[M], 2002, Beijing, Metallurgic Industry Press(in Chinese)

[17] YangChun-hua, DeconinckG, GuiWei-hua. An optimal power-dispatching control system for the electrochemical process of zinc based on backpropagation and hopfield neural networks[J]. IEEE Transactions on Industrial Electronics, 2003, 50(5): 953-961

[18] HuZhi-kum, PengXiao-qi, GuiWei-hua. Neural network based on chaotic gradient optimization algorithm and its application[J]. Journal of Central South University of Technology, 2004, 11(4): 93-96