NonSysId提升非线性系统建模精度，无需验证集也能选优模型。
NonSysId: A nonlinear system identification package with improved model term selection for NARMAX models
- 用迭代正交前向回归结合PRESS统计量选模型项
- 在无验证数据下仍保持高仿真准确率和简洁模型
- 适合结构健康监测等实时场景，抗数据不全
系统辨识通过输入输出数据构建动态系统的数学模型，可实现对系统行为的时间与频域分析。为保证分析有效性，模型需准确反映系统本质特性。本文提出NonSysId，一个开源的MATLAB软件包，专用于非线性系统辨识，聚焦于NARMAX模型。该工具采用先进项选择方法，在优先保障仿真（自由运行）精度的同时保持模型简洁性。核心特征是将迭代正交前向回归（iOFR）与基于预测残差平方和（PRESS）的项选择相结合，实现无需独立验证数据即可获得鲁棒泛化能力的模型。同时，引入降低计算开销的技术。这些特性使NonSysId特别适用于结构健康监测、故障诊断及生物信号处理等实时应用，尤其在难以获取一致条件下的信号数据，导致可用验证数据极少或缺失的场景中具有优势。
原文摘要 · Abstract (English)
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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