Billings, S.A., Wei, H.L. and Balikhin, M.A. (2005) Multi-scale Radial Basis Function Networks. Research Report. ACSE Research Report 894 . Department of Automatic Control and Systems Engineering
Abstract
A novel modelling framework is proposed for constructing parsimonious and flexible radial basis function network (RBF) models. Unlike a conventional standard Gaussian kernel based RBF network, where all the basis functions have the same scale (kernel width), or each basis function has a single individual scale, the new network construction approach adopts multiscale kernels (with multiple kernel widths for each selected centre) as the basis functions to provide more flexible representations with better generalized properties for general nonlinear dynamical systems. A standard orthogonal least squares (OLS) algorithm is then applied to select significant model terms (basis functions) to obtain parsimonious models.
Metadata
Item Type: | Monograph |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. |
Keywords: | Dynamical modelling; Model term selection; Neural network; Nonlinear system identification; Orthogonal least squares; Radial basis function; Regression |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports |
Depositing User: | MRS ALISON THERESA BARNETT |
Date Deposited: | 24 Apr 2015 11:37 |
Last Modified: | 27 Oct 2016 14:37 |
Status: | Published |
Publisher: | Department of Automatic Control and Systems Engineering |
Series Name: | ACSE Research Report 894 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85346 |