Universiti Teknologi Malaysia Institutional Repository

Model structure selection for a discrete-time non-linear system using genetic algorithm

Ahmad, Robiah and Jamaluddin , Hishamuddin and Hussain, Mohd. Azlan (2004) Model structure selection for a discrete-time non-linear system using genetic algorithm. Proc. Instn Mech. Engrs, J. Systems and Control Engineering, 218 (12). pp. 85-98.


Official URL: http://dx.doi.org/10.1243/095965104322892258


In recent years, extensive works on genetic algorithms have been reported covering various applications. Genetic algorithms (GAs) have received significant interest from researchers and have been applied to various optimization problems. They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. First the effect of different combinations of GA operators on the performance of the model developed is studied. A proposed algorithm called modified GA, or MGA, is presented and a comparison between a simple GA and a modified GA is carried out. The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. The adequacy of the developed models is tested using one-step-ahead prediction and correlation-based model validation tests. The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.

Item Type:Article
Uncontrolled Keywords:model structure selection, system identification, model structure selection, system identification, evolutionary programming, genetic algorithms
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Mechanical Engineering
ID Code:7107
Deposited By: Dr. Robiah Ahmad
Deposited On:06 Mar 2017 08:37
Last Modified:06 Mar 2017 08:41

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