genetic programming is combined with continuum
regression to produce two novel non-linear 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
non-linear 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. These applications clearly demonstrate
that the team-based continuum regression strategy
significantly outperforms both sequential approaches.
%0 Journal Article
%1 Mckay:2000:TIMC
%A McKay, Ben
%A Willis, Mark
%A Searson, Dominic
%A Montague, Gary
%D 2000
%J Transactions of the Institute of Measurement and
Control
%K algorithms, continuum genetic modelling process programming, regression,
%N 2
%P 125--140
%R doi:10.1177/014233120002200202
%T Nonlinear continuum regression: an evolutionary
approach
%U http://www.ingentaconnect.com/content/arn/tm/2000/00000022/00000002/art00007
%V 22
%X genetic programming is combined with continuum
regression to produce two novel non-linear 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
non-linear 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. These applications clearly demonstrate
that the team-based continuum regression strategy
significantly outperforms both sequential approaches.
@article{Mckay:2000:TIMC,
abstract = {genetic programming is combined with continuum
regression to produce two novel non-linear 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
non-linear 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. These applications clearly demonstrate
that the team-based continuum regression strategy
significantly outperforms both sequential approaches.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {McKay, Ben and Willis, Mark and Searson, Dominic and Montague, Gary},
biburl = {https://www.bibsonomy.org/bibtex/2c4a61fc05ec821e452816267e9faeb53/brazovayeye},
doi = {doi:10.1177/014233120002200202},
email = {mark.willis@ncl.ac.uk},
interhash = {baad382bc57a9f31ffc2ee2c4b97679f},
intrahash = {c4a61fc05ec821e452816267e9faeb53},
journal = {Transactions of the Institute of Measurement and
Control},
keywords = {algorithms, continuum genetic modelling process programming, regression,},
number = 2,
pages = {125--140},
timestamp = {2008-06-19T17:46:39.000+0200},
title = {Nonlinear continuum regression: an evolutionary
approach},
url = {http://www.ingentaconnect.com/content/arn/tm/2000/00000022/00000002/art00007},
volume = 22,
year = 2000
}