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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 831657, 12 pages
http://dx.doi.org/10.1155/2013/831657
Research Article

A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

1Department of Mechatronics Engineering, Faculty of Engineering, The University of Jordan, Amman 11942, Jordan
2Department of Electrical Engineering, Faculty of Engineering, The University of Jordan, Amman 11942, Jordan
3Department of Mathematics, Faculty of Science, The University of Jordan, Amman 11942, Jordan
4Department of Mathematics, Faculty of Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia
5Department of Mathematics, Faculty of Science, Al Balqa Applied University, Salt 19117, Jordan

Received 19 June 2013; Accepted 9 October 2013

Academic Editor: Ben T. Nohara

Copyright © 2013 Za'er Abo-Hammour et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.