A Simplified Volterra Identification Model of Nonlinear System

DOI

10.2991/icmeit-17.2017.114How to use a DOI?

Keywords

Nonlinear system; Volterra series model; nonparametric model identification; Random multi-tone excitation.

Abstract

Conventional Volterra series model is hardly applied to engineering practice due to its parametric complexity and estimation difficulty. To solve this problem, nonlinear system identification using reduced complexity Volterra models is proposed. Since the nonlinear components often play a secondary role compared to the dominant, linear component of the system, they spend the most of identification cost. So it is worth establishing a balance between identification cost and model accuracy by reducing the complexity of nonlinear components. Refer to the idea of nonlinear output frequency response function, conventional Volterra model is simplified. And then a minimum mean square error criterion based method to identify the simplified model is proposed. The distinguishing feature of this method is high accuracy, good robustness, and significant reduction in the computational requirements compare to the identification of conventional Volterra models. The simulation show that the proposed method is effective, and the reduced complexity Volterra model is of good generalization ability in general. So this nonlinear system identification approach is quite applicable to engineering practice.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guodong Jin
AU  - Libin Lu
AU  - Xiaofei Zhu
PY  - 2017/05
DA  - 2017/05
TI  - A Simplified Volterra Identification Model of Nonlinear System
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 622
EP  - 625
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.114
DO  - 10.2991/icmeit-17.2017.114
ID  - Jin2017/05
ER  -