Effect of Penalty Function Parameter in Objective Function of System Identification

Authors

  • M.F. Abd Samad
  • H. Jamaluddin
  • R. Ahmad
  • M.S. Yaacob
  • A.K.M. Azad

DOI:

https://doi.org/10.15282/ijame.7.2012.0076

Keywords:

Genetic algorithm; objective function; penalty function; model structure selection; system identification

Abstract

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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Published

2022-12-09

How to Cite

[1]
M.F. Abd Samad, H. Jamaluddin, R. Ahmad, M.S. Yaacob, and A.K.M. Azad, “Effect of Penalty Function Parameter in Objective Function of System Identification”, Int. J. Automot. Mech. Eng., vol. 7, Dec. 2022.

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