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Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

Hong, X. and Harris, C. J. (2002) Nonlinear model structure design and construction using orthogonal least squares and D-optimality design. IEEE Transactions on Neural Networks, 13 (5). pp. 1245-1250. ISSN 1045-9227

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To link to this item DOI: 10.1109/TNN.2002.1031959


A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:18498
Uncontrolled Keywords:D-optimality design , RBF neural net , composite cost function , computational efficiency , design matrix , experimental design , fuzzy neural networks , learning algorithm , model approximation , model parameter estimation , model robustness , model subset selection , model subset selection cost function , nonlinear model structure design , orthogonal least squares

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