Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 17, Issue 4, Pages 363-370Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2004.04.009
Keywords
system modelling; genetic programming; genetic algorithms; parameter estimation
Ask authors/readers for more resources
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. (C) 2004 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available