EFFECT OF PENALTY FUNCTION PARAMETER IN OBJECTIVE FUNCTION OF SYSTEM IDENTIFICATION
The evaluation of an objective function for a particular model allows one to determine the optimality of a model structure with the aim of selecting an adequate model in system identification. Recently, an objective function was introduced that, besides evaluating predictive accuracy, includes a log...
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| Format: | Article |
| Language: | English |
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Universiti Malaysia Pahang
2013
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| Online Access: | http://eprints.utem.edu.my/id/eprint/9716/1/Paper_penalty_IJAME.pdf http://eprints.utem.edu.my/id/eprint/9716/ |
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| Summary: | The evaluation of an objective function for a particular model allows one to determine the optimality of a model structure with the aim of selecting an adequate model in system identification. Recently, an objective function was introduced that, besides evaluating predictive accuracy, includes a logarithmic penalty function to achieve a suitable balance between the former model’s characteristics and model parsimony. However, the parameter value in the penalty function was made arbitrarily. This paper presents a study on the effect of the penalty function parameter in model structure selection in system identification on a number of simulated models. The search was done using genetic algorithms. A representation of the sensitivity of the penalty function parameter value in model structure selection is given, along with a proposed mathematical function that defines it. A recommendation is made regarding how a suitable penalty function parameter value can be determined. |
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