A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem
Created by W.Langdon from
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- @Article{Metenidis:2004:EAAI,
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author = "Mihai Florin Metenidis and Marcin Witczak and
Jozef Korbicz",
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title = "A novel genetic programming approach to nonlinear
system modelling: application to the {DAMADICS}
benchmark problem",
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journal = "Engineering Applications of Artificial Intelligence",
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year = "2004",
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volume = "17",
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pages = "363--370",
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number = "4",
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month = jun,
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note = "Selected Problems of Knowledge Representation",
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keywords = "genetic algorithms, genetic programming, system
modelling, Parameter estimation",
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ISSN = "0952-1976",
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owner = "wlangdon",
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broken = "http://www.sciencedirect.com/science/article/B6V2M-4CKFH3B-1/2/49d6ac641b4455bbc65260281aa1ee55",
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DOI = "doi:10.1016/j.engappai.2004.04.009",
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abstract = "Nonlinear system modelling is a diverse research area
where different kinds of methodologies can be employed.
However, due to the large variety of this field, no
approach imposes itself as the best one. The difficulty
of system modelling consists in the necessity of
approximating both the structure and the parameters of
a system. That is why the choice of the approach to be
used usually depends on a specific application. This
paper presents a modified genetic programming approach
for model structure selection combined with a classical
technique for parameter estimation. In particular,
various combinations of parameterised fixed length
trees are proposed as candidate model structures. The
algorithms that can be used to obtain a suitable form
of these structures are proposed as well. The final
part of the paper justifies the possibility of using
this approach in practice, i.e. a comprehensive
empirical study is performed with the data acquired
from an industrial actuator.",
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notes = "Also known as \cite{FLORINMETENIDIS2004363}",
- }
Genetic Programming entries for
Mihai Florin Metenidis
Marcin Witczak
Jozef Korbicz
Citations