Improved Model Order Selection in Dynamical System Identification Based on Trend Extraction

Model order selection plays a crucial role in system identification. The existing model order selection methods rely on finding the balance between fitting error and model complexity. However, when the data contains large noise, the model order obtained by the existing methods may not be reliable. To resolve this issue, we present a new model order selection method based on trend error analysis. By making use of a specific property of trend extraction — the insensitivity against noise, our method improves the accuracy of model order selection. Numerical simulation results show the Effectiveness of the proposed method and outperformance over known heuristics under different noise levels.