A systematic neuro-fuzzy modeling framework with application to material property prediction

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.

[1]  C. L. Philip Chen,et al.  Materials structure-property prediction using a self-architecting neural network , 1998 .

[2]  Derek A. Linkens,et al.  Input selection and partition validation for fuzzy modelling using neural network , 1999, Fuzzy Sets Syst..

[3]  W. Pedrycz,et al.  Construction of fuzzy models through clustering techniques , 1993 .

[4]  Ching-Chang Wong,et al.  A hybrid clustering and gradient descent approach for fuzzy modeling , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Derek A. Linkens,et al.  Learning control using fuzzified self-organizing radial basis function network , 1993, IEEE Trans. Fuzzy Syst..

[6]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[7]  Bhavik R. Bakshi,et al.  Unification of neural and statistical methods as applied to materials structure-property mapping , 1998 .

[8]  Si-Zhao Joe Qin,et al.  A multiregion fuzzy logic controller for nonlinear process control , 1994, IEEE Trans. Fuzzy Syst..

[9]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[10]  L. Zadeh A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges , 1972 .

[11]  Li-Xin Wang Modeling and control of hierarchical systems with fuzzy systems , 1997, Autom..

[12]  Yinghua Lin,et al.  Using fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural network , 1997, IEEE Trans. Fuzzy Syst..

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Stefano Marsili-Libelli,et al.  Adaptive fuzzy pattern recognition in the anaerobic digestion process , 1996, Pattern Recognit. Lett..

[15]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[16]  J. Bezdek Cluster Validity with Fuzzy Sets , 1973 .

[17]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[19]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[20]  Michael P. Windham,et al.  Cluster Validity for the Fuzzy c-Means Clustering Algorithrm , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[22]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[23]  Harpreet Singh,et al.  A neuro fuzzy logic approach to material processing , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[24]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[25]  Yinghua Lin,et al.  A new approach to fuzzy-neural system modeling , 1995, IEEE Trans. Fuzzy Syst..