A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem

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@Article{Metenidis:2004:EAAI,

• author = "Mihai Florin Metenidis and Marcin Witczak and Jozef Korbicz",
• title = "A novel genetic programming approach to nonlinear system modelling: application to the {DAMADICS} benchmark problem",
• journal = "Engineering Applications of Artificial Intelligence",
• year = "2004",
• volume = "17",
• pages = "363--370",
• number = "4",
• month = jun,
• note = "Selected Problems of Knowledge Representation",
• keywords = "genetic algorithms, genetic programming, system modelling, Parameter estimation",
• ISSN = "0952-1976",
• owner = "wlangdon",
• broken = "http://www.sciencedirect.com/science/article/B6V2M-4CKFH3B-1/2/49d6ac641b4455bbc65260281aa1ee55",
• DOI = "doi:10.1016/j.engappai.2004.04.009",
• 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.",
• notes = "Also known as \cite{FLORINMETENIDIS2004363}",

}

Genetic Programming entries for Mihai Florin Metenidis Marcin Witczak Jozef Korbicz

Citations