Title :
Industrial applications of fuzzy system modeling
Author_Institution :
Dept. of Mech. & Ind. Eng., Toronto Univ., Ont., Canada
Abstract :
Aggregate industrial system behaviour models can be built with fuzzy data mining provided the historical system behaviour data are available from system databases. Given the input-output data vectors, a unified system modeling approach can be used to extract “hidden rules” of system behaviour using fuzzy technology. In particular, fuzzy cluster analysis could be used with unsupervised learning to extract fuzzy set membership function and the fuzzy rule structures. A parametric reasoning method combined with supervised learning with minimum error criteria could determine combination operators. This eliminates the arbitrary choice of t-norms and t-conorms that are required in the execution of approximate reasoning algorithms. Examples given include continuous caster scheduling in steel making with criteria of minimum tardiness and minimum mixed grade steel production. This methodology can also be applied to pharmacological analysis of experimental data
Keywords :
computer aided production planning; data mining; fuzzy set theory; fuzzy systems; inference mechanisms; learning (artificial intelligence); production control; approximate reasoning; continuous casting; data mining; fuzzy cluster analysis; fuzzy set theory; fuzzy system; industrial system behaviour models; membership function; minimum error criteria; parametric reasoning; steel making; system modeling; t-conorms; t-norms; unsupervised learning; Aggregates; Data mining; Databases; Fuzzy sets; Fuzzy systems; Mining industry; Modeling; Steel; Supervised learning; Unsupervised learning;
Conference_Titel :
Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-5489-3
DOI :
10.1109/IPMM.1999.792469
