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JACIII Vol.16 No.4 pp. 489-495
doi: 10.20965/jaciii.2012.p0489
(2012)

Paper:

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Nonlinear Dynamic System Identification Using Volterra Series: Multi-Objective Optimization Approach

Sayed Mohammad Reza Loghmanian*,**, Rubiyah Yusof**,
and Marzuki Khalid**

*Faculty of Engineering, Islamic Azad University, Mobarakeh Branch, Isfahan, Iran

**Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, International Campus, Jalan Semarak, 54100 Kuala Lumpur, Malaysia

Received:
December 28, 2011
Accepted:
April 15, 2012
Published:
June 20, 2012
Creative Commons License
Keywords:
volterra series, nonlinear system, multiobjective optimization, system identification, dynamic system
Abstract
In this paper, identification of the nonlinear dynamic systems based on an optimized Volterra model structure is presented. Model structure selection is an important step in system identification, which involves the selection of variables and terms of a model. The key task is choosing a compact model representation where only significant terms are selected from among all the possible ones while also taking good performance into account. An automated algorithm based on multi-objective optimization is proposed for this purpose. The developed model should fulfill two criteria or objectives, namely, high prediction accuracy and optimum model structure. A genetic algorithm is applied to search the significant Volterra kernels from among all possible candidate model combinations. The result shows that the proposed algorithm is able to correctly identify the simulated examples and adequately model the nonlinear discrete dynamic system.
Cite this article as:
S. Loghmanian, R. Yusof, and M. Khalid, “Nonlinear Dynamic System Identification Using Volterra Series: Multi-Objective Optimization Approach,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.4, pp. 489-495, 2012.
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