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Modified radial basis function neural network using output transformation

Hong, X. (2007) Modified radial basis function neural network using output transformation. IET Control Theory and Applications, 1 (1). pp. 1-8. ISSN 1751-8644

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To link to this item DOI: 10.1049/iet-cta:20050039

Abstract/Summary

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15266
Uncontrolled Keywords:ORTHOGONAL LEAST-SQUARES, EXPERIMENTAL-DESIGN, REGRESSION, ALGORITHM, REGULARIZATION

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