title: A systematic neuro-fuzzy modeling framework with application to material property prediction.
abstract: A systematic neural-fuzzy modeling framework that includes the initial fuzzy model self-generation, significant input selection, partition validation, parameter optimization, and rule-base simplification is proposed in this paper. In this framework, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of a sell-organization network, fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. The proposed neuro-fuzzy modeling approach has been used for nonlinear system identification and mechanical property prediction in hot-rolled steels from construct composition and microstructure data. Experimental studies demonstrate that the predicted mechanical properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules.
authors.affiliation: Dept. of Autom. Control & Syst. Eng., Sheffield Univ.
authors.forename: M Y
authors.initials: MY
authors.lastname: Chen
authors.forename: D A
authors.initials: DA
authors.lastname: Linkens