Paper Info
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 self-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.
Year
DOI
Venue
2001
10.1109/3477.956039
IEEE Transactions on Systems, Man, and Cybernetics, Part B
Keywords
DocType
Volume
nonlinear system identification,fuzzy clustering,fuzzy logic,mathematical model,material properties,rule based,fuzzy sets,knowledge based systems,predictive models,backpropagation,indexing terms,microstructures,neuro fuzzy,nonlinear systems,back propagation,fuzzy systems
Journal
31
Issue
ISSN
Citations 
5
1083-4419
44
PageRank 
References 
Authors
2.77
17
2
Name
Order
Citations
PageRank
Min-you Chen127422.18
Derek A. Linkens221525.36