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Empirical data modelling algorithms: Additive spline models and support vector machines

Empirical data modelling algorithms: Additive spline models and support vector machines
Empirical data modelling algorithms: Additive spline models and support vector machines
Empirical data modelling techniques are widely used in the control field, from simple white-box, linear parameter identification schemes to black-box non-linear models. Non-linear, semi-parametric model building algorithms have been extensively studied over the past ten years, and despite their success in many applications where prior information is lacking or incorrect, verification and validation is notoriously difficult. One of the key aspects of verification and validation is transparency, where the network's generalisation abilities are explicitly represented. This paper describes two approaches for building an ANOVA representation of non-linear, multivariate data: one based on forwards selection and backwards elimination spline models and the other using a support vector machine with an ANOVA-kernel decomposition.
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Gunn, S. R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Gunn, S. R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868

Brown, M. and Gunn, S. R. (1998) Empirical data modelling algorithms: Additive spline models and support vector machines. UKACC Int. Conf. on Control '98.

Record type: Conference or Workshop Item (Other)

Abstract

Empirical data modelling techniques are widely used in the control field, from simple white-box, linear parameter identification schemes to black-box non-linear models. Non-linear, semi-parametric model building algorithms have been extensively studied over the past ten years, and despite their success in many applications where prior information is lacking or incorrect, verification and validation is notoriously difficult. One of the key aspects of verification and validation is transparency, where the network's generalisation abilities are explicitly represented. This paper describes two approaches for building an ANOVA representation of non-linear, multivariate data: one based on forwards selection and backwards elimination spline models and the other using a support vector machine with an ANOVA-kernel decomposition.

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More information

Published date: 1998
Additional Information: Address: Swansea, UK
Venue - Dates: UKACC Int. Conf. on Control '98, 1998-01-01
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 250628
URI: https://eprints.soton.ac.uk/id/eprint/250628
PURE UUID: fc536cda-fd47-4d6b-abed-115ec778bdfe

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Date deposited: 25 Jun 1999
Last modified: 16 Jul 2019 23:11

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