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
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.
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