A type-2 fuzzy expert system based on a hybrid inference method for steel industry

In this paper, a novel type-2 fuzzy expert system for prediction the amount of reagents in desulfurization process of a steel industry in Canada is developed. In this model, the new interval type-2 fuzzy c-regression clustering algorithm for structure identification phase of Takagi–Sugeno (T–S) syst...

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Veröffentlicht in:	International journal of advanced manufacturing technology Jg. 71; H. 5-8; S. 857 - 885
Hauptverfasser:	Fazel Zarandi, M. H., Gamasaee, R., Turksen, I. B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.03.2014 Springer Nature B.V
Schlagworte:	Adaptive systems Algorithms CAE) and Design Canonical forms Clustering Computer-Aided Engineering (CAD Engineering Error reduction Expert systems Fuzzy systems Hybrid systems Industrial and Production Engineering Industrial development Inference Iron and steel industry Mechanical Engineering Media Management Neural networks Original Article Probabilistic models Reagents Statistical analysis Statistical tests Steel industry Interval type-2 fuzzy hybrid system Fuzzy disjunctive normal forms IT2F c-regression clustering Steel industry Gaussian mixture model Fuzzy conjunctive normal forms
ISSN:	0268-3768, 1433-3015
Online-Zugang:	Volltext
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