Nonlinear System Identification Based on Reduced Complexity Volterra Models
Guodong Jin, Libin Lu
Available Online February 2016.
- https://doi.org/10.2991/emcm-15.2016.72How to use a DOI?
- Nonlinear system; Volterra series model; Nonparametric model identification; Random multi-tone excitation
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Guodong Jin AU - Libin Lu PY - 2016/02 DA - 2016/02 TI - Nonlinear System Identification Based on Reduced Complexity Volterra Models BT - International Conference on Electronics, Mechanics, Culture and Medicine PB - Atlantis Press SP - 386 EP - 390 SN - 2352-538X UR - https://doi.org/10.2991/emcm-15.2016.72 DO - https://doi.org/10.2991/emcm-15.2016.72 ID - Jin2016/02 ER -