Using Genetic Programming to Develop Inferential
Estimation Algorithms
B. McKay, M. Willis, G. Montague, и G. Barton. Genetic Programming 1996: Proceedings of the First
Annual Conference, стр. 157--165. Stanford University, CA, USA, MIT Press, (28--31 July 1996)
Аннотация
Genetic Programming (GP) is used to develop
inferential estimation algorithms for two industrial
chemical processes. Within this context, dynamic
modelling procedures (as opposed to static or
steady-state modelling) are often required if accurate
inferential models are to be developed. Thus, a simple
procedure is suggested so that the GP technique may be
used for the development of dynamic process models.
Using measurements from a vacuum distillation column
and an industrial plasticating extrusion process, it is
then demonstrated how the GP methodology can be used to
develop reliable cost effective process models. A
statistical analysis procedure is used to aid in the
assessment of GP algorithm settings and to guide in the
selection of the final model structure.
%0 Conference Paper
%1 mckay:1996:GPidea
%A McKay, Ben
%A Willis, Mark
%A Montague, Gary
%A Barton, Geoffrey W.
%B Genetic Programming 1996: Proceedings of the First
Annual Conference
%C Stanford University, CA, USA
%D 1996
%E Koza, John R.
%E Goldberg, David E.
%E Fogel, David B.
%E Riolo, Rick L.
%I MIT Press
%K algorithms, genetic programming
%P 157--165
%T Using Genetic Programming to Develop Inferential
Estimation Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X Genetic Programming (GP) is used to develop
inferential estimation algorithms for two industrial
chemical processes. Within this context, dynamic
modelling procedures (as opposed to static or
steady-state modelling) are often required if accurate
inferential models are to be developed. Thus, a simple
procedure is suggested so that the GP technique may be
used for the development of dynamic process models.
Using measurements from a vacuum distillation column
and an industrial plasticating extrusion process, it is
then demonstrated how the GP methodology can be used to
develop reliable cost effective process models. A
statistical analysis procedure is used to aid in the
assessment of GP algorithm settings and to guide in the
selection of the final model structure.
@inproceedings{mckay:1996:GPidea,
abstract = {Genetic Programming (GP) is used to develop
inferential estimation algorithms for two industrial
chemical processes. Within this context, dynamic
modelling procedures (as opposed to static or
steady-state modelling) are often required if accurate
inferential models are to be developed. Thus, a simple
procedure is suggested so that the GP technique may be
used for the development of dynamic process models.
Using measurements from a vacuum distillation column
and an industrial plasticating extrusion process, it is
then demonstrated how the GP methodology can be used to
develop reliable cost effective process models. A
statistical analysis procedure is used to aid in the
assessment of GP algorithm settings and to guide in the
selection of the final model structure.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Stanford University, CA, USA},
author = {McKay, Ben and Willis, Mark and Montague, Gary and Barton, Geoffrey W.},
biburl = {https://www.bibsonomy.org/bibtex/2d3a405c13defbc6c93fef0a0058a73a8/brazovayeye},
booktitle = {Genetic Programming 1996: Proceedings of the First
Annual Conference},
broken = {http://lorien.ncl.ac.uk/sorg/paper2.ps},
editor = {Koza, John R. and Goldberg, David E. and Fogel, David B. and Riolo, Rick L.},
interhash = {65c1f7e3c4afd101305a9da78799613a},
intrahash = {d3a405c13defbc6c93fef0a0058a73a8},
keywords = {algorithms, genetic programming},
month = {28--31 July},
notes = {GP-96, MSWord postscript not cmpatible with Unix},
pages = {157--165},
publisher = {MIT Press},
size = {9 pages},
timestamp = {2008-06-19T17:46:38.000+0200},
title = {Using Genetic Programming to Develop Inferential
Estimation Algorithms},
url = {http://cognet.mit.edu/library/books/view?isbn=0262611279},
year = 1996
}