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Nonlinear dynamic system identification using Volterra series: multi-objective optimization approach

Loghmanian, S. M. R. and Yusof, Rubiyah and Khalid, M. (2011) Nonlinear dynamic system identification using Volterra series: multi-objective optimization approach. In: 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization. IEEE Xplore, Red Hook, NY, 001-005. ISBN 978-1-4577-0003-3

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Official URL: http://ieeexplore.ieee.org/document/5775636/


In this paper, system identification of the non-linear dynamic system based on optimized Volterra model structure is considered. Model structure selection is an important step in system identification, which involves the selection of variables and terms of a model. The important issue is choosing a compact model representation where only significant terms are selected among all the possible ones beside good performance. An automated algorithm based on multi-objective optimization is proposed. The developed model should fulfil two criteria or objectives namely good predictive accuracy and optimum model structure. Genetic algorithm is applied to search the significant Volterra kernels 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.

Item Type:Book Section
Uncontrolled Keywords:volterra series, nonlinear system, multi-objective optimization, system identification, dynamic system
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:29507
Deposited By: Maznah Mohamed Ali
Deposited On:19 Mar 2013 00:44
Last Modified:27 Jul 2017 04:35

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