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Lookup NU author(s): Ben McKay, Dr Mark Willis, Dr Dominic Searson, Professor Gary Montague
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In this contribution, genetic programming is combined with continuum regression to produce two novel nonlinear continuum regression algorithms. The first is a 'sequential' algorithm while the second adopts a 'team-based' strategy. Having discussed continuum regression, the modifications required to extend the algorithm for nonlinear modelling are outlined. The results of two case studies are then presented: the development of an inferential model of a food extrusion process and an input-output model of an industrial bioreactor. The superior performance of the sequential continuum regression algorithm, as compared to a similar sequential nonlinear partial least squares algorithm, is demonstrated. In addition, the studies clearly demonstrate that the team-based continuum regression strategy significantly out-performs both sequential approaches.
Author(s): McKay B, Willis M, Searson D, Montague G
Publication type: Article
Publication status: Published
Journal: Transactions of the Institute of Measurement and Control
Year: 2000
Volume: 22
Issue: 2
Pages: 125-140
ISSN (print): 0142-3312
ISSN (electronic): 1477-0369
Publisher: Sage Publications Ltd.
URL: http://dx.doi.org/10.1177/014233120002200202
DOI: 10.1177/014233120002200202
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