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Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

Hong, X., Harris, C. J. and Chen, S. (2004) Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 34 (1). pp. 598-608. ISSN 1083-4419

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To link to this item DOI: 10.1109/tsmcb.2003.817089

Abstract/Summary

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15281
Uncontrolled Keywords:neurofuzzy networks, optimal experimental design, orthogonal, decomposition, regularization, subspace, ORTHOGONAL LEAST-SQUARES, IDENTIFICATION, SYSTEMS, REDUCTION, NETWORKS

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