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A fast identification algorithm for Box-Cox transformation based radial basis function neural network

Hong, X. (2006) A fast identification algorithm for Box-Cox transformation based radial basis function neural network. IEEE Transactions on Neural Networks, 17 (4). pp. 1064-1069. ISSN 1045-9227

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To link to this item DOI: 10.1109/tnn.2006.875986

Abstract/Summary

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15265
Uncontrolled Keywords:Box-Cox transform, forward regression, Gauss-Newton algorithm, QR, decomposition, radial basis function, subset selection, ORTHOGONAL LEAST-SQUARES, EXPERIMENTAL-DESIGN

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