Nonlinear continuum regression: an evolutionary approach
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 outperforms both sequential approaches.
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Article first published: June 2000
Issue published: June 2000
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This article was published in Transactions of the Institute of Measurement and Control.
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