NonSysId: A nonlinear system identification package with improved model term selection for NARMAX models

• URL: http://arxiv.org/abs/2411.16475v1
• Date: Mon, 25 Nov 2024 15:19:19 GMT
• Title: NonSysId: A nonlinear system identification package with improved model term selection for NARMAX models
• Authors: Rajintha Gunawardena, Zi-Qiang Lang, Fei He,
• Abstract summary: This paper introduces NonSysId, an open-sourced software package designed for nonlinear system identification. The software incorporates an advanced term selection methodology that prioritises on simulation (free-run) accuracy while preserving model parsimony. NonSysId is particularly suitable for real-time applications such as structural health monitoring, fault diagnosis, and biomedical signal processing.
• Score: 2.6684288899870543
• License:
• Abstract: System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential for the model to accurately reflect the underlying system's behaviour. This paper introduces NonSysId, an open-sourced MATLAB software package designed for nonlinear system identification, specifically focusing on NARMAX models. The software incorporates an advanced term selection methodology that prioritises on simulation (free-run) accuracy while preserving model parsimony. A key feature is the integration of iterative Orthogonal Forward Regression (iOFR) with Predicted Residual Sum of Squares (PRESS) statistic-based term selection, facilitating robust model generalisation without the need for a separate validation dataset. Furthermore, techniques for reducing computational overheads are implemented. These features make NonSysId particularly suitable for real-time applications such as structural health monitoring, fault diagnosis, and biomedical signal processing, where it is a challenge to capture the signals under consistent conditions, resulting in limited or no validation data.

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