Genetic Programming (GP), a heuristic optimisation
technique based on the theory of Genetic Algorithms
(GAs), is a method successfully used to identify
non-linear model structures by analysing a system's
measured signals. Mostly, it is used as an offline tool
that means that structural analysis is done after
collecting all available identification data. In this
paper, we propose an enhanced on-line GP approach that
is able to adapt its behaviour to new observations
while the GP process is executed. Furthermore, an
approach using GP for online Fault Diagnosis (FD) is
described, and finally test results using measurement
data of NO<SUB align=right><SMALL>x</SMALL></SUB>
emissions of a BMW diesel engine are discussed.
%0 Journal Article
%1 oai:inderscience.com:12487
%A Winkler, Stephan
%A Efendic, Hajrudin
%A Del
Re, Luigi
%A Affenzeller, Michael
%A Wagner, Stefan
%D 2007
%I Inderscience Publishers
%J International Journal of Intelligent Systems
Technologies and Applications
%K GP, algorithms, automatic data diagnosis, driven fault genetic identification, learning, machine model modelling, online programming, real self-adaption, time
%N 2/3
%P 255--270
%R doi:10.1504/IJISTA.2007.012487
%T Online modelling based on Genetic Programming
%U http://www.inderscience.com/link.php?id=12487
%V 2
%X Genetic Programming (GP), a heuristic optimisation
technique based on the theory of Genetic Algorithms
(GAs), is a method successfully used to identify
non-linear model structures by analysing a system's
measured signals. Mostly, it is used as an offline tool
that means that structural analysis is done after
collecting all available identification data. In this
paper, we propose an enhanced on-line GP approach that
is able to adapt its behaviour to new observations
while the GP process is executed. Furthermore, an
approach using GP for online Fault Diagnosis (FD) is
described, and finally test results using measurement
data of NO<SUB align=right><SMALL>x</SMALL></SUB>
emissions of a BMW diesel engine are discussed.
@article{oai:inderscience.com:12487,
abstract = {Genetic Programming (GP), a heuristic optimisation
technique based on the theory of Genetic Algorithms
(GAs), is a method successfully used to identify
non-linear model structures by analysing a system's
measured signals. Mostly, it is used as an offline tool
that means that structural analysis is done after
collecting all available identification data. In this
paper, we propose an enhanced on-line GP approach that
is able to adapt its behaviour to new observations
while the GP process is executed. Furthermore, an
approach using GP for online Fault Diagnosis (FD) is
described, and finally test results using measurement
data of NO<SUB align=right><SMALL>x</SMALL></SUB>
emissions of a BMW diesel engine are discussed.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Winkler, Stephan and Efendic, Hajrudin and {Del
Re}, Luigi and Affenzeller, Michael and Wagner, Stefan},
bibsource = {OAI-PMH server at www.inderscience.com},
biburl = {https://www.bibsonomy.org/bibtex/20de5658a625e576c7f964d1b6ebf1424/brazovayeye},
doi = {doi:10.1504/IJISTA.2007.012487},
interhash = {77df956a0f8665b7a5dcbf2578c022b9},
intrahash = {0de5658a625e576c7f964d1b6ebf1424},
issn = {1740-8873},
journal = {International Journal of Intelligent Systems
Technologies and Applications},
keywords = {GP, algorithms, automatic data diagnosis, driven fault genetic identification, learning, machine model modelling, online programming, real self-adaption, time},
language = {eng},
month = {19 February},
number = {2/3},
oai = {oai:inderscience.com:12487},
pages = {255--270},
publisher = {Inderscience Publishers},
relation = {ISSN online: 1740-8873 ISSN print: 1740-8865 DOI:
10.1504/07.12487},
rights = {Inderscience Copyright},
source = {IJISTA (2007), Vol 2 Issue 2/3, pp 255 - 270},
timestamp = {2008-06-19T17:54:16.000+0200},
title = {Online modelling based on Genetic Programming},
url = {http://www.inderscience.com/link.php?id=12487},
volume = 2,
year = 2007
}