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Nonlinear continuum regression: an evolutionary approach

Lookup NU author(s): Ben McKay, Dr Mark Willis, Dr Dominic Searson, Professor Gary Montague

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Abstract

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.


Publication metadata

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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