AI 中文总结
本文提出递归形式的SMI-IPCA（RSMI-IPCA），通过维护固定长度滞后窗口，能在线估计测量噪声方差、过程阶数并识别状态空间矩阵，适用于监测时变系统，仿真研究验证了该算法的有效性和适用性。
AI 中文摘要
基于子空间的模型辨识算法（SMI-IPCA）是一种理论严谨的方法，用于在变量含误差（EIV）设置下识别多输入多输出（MIMO）过程的线性状态空间模型，可同时估计输入和输出测量中的未知异方差噪声方差及状态空间模型。本文提出递归形式的SMI-IPCA（RSMI-IPCA）实现在线辨识和自适应模型更新。通过维护固定长度滞后窗口而非存储完整历史数据，RSMI-IPCA能估计测量噪声方差、过程阶数并识别状态空间矩阵，适用于监测时变系统。仿真研究证明了该算法的有效性和实际适用性。
英文摘要
The Subspace-based Model Identification algorithm using a modified Iterative Principal Component Analysis (SMI-IPCA) is a theoretically rigorous method for identifying a linear state-space model of a multi-input multi-output (MIMO) process, in an errors-in-variables (EIV) setting. The method can simultaneously estimate unknown heteroskedastic noise variances corrupting the input and output measurements, along with the state space model. This work proposes a recursive formulation of SMI-IPCA (RSMI-IPCA) enabling online identification and adaptive model updates as and when new data arrive. By maintaining a fixed length lag window rather than storing the complete historical data, RSMI-IPCA estimates measurement noise variances, process order, while simultaneously identifying the state-space matrices, making it suitable to monitor time-varying systems, whether the induced changes are slow or abrupt. The algorithm gradually adapts to slow sensor degradation (time-varying noise variances), changes in process operating conditions (time-varying model parameters), and structural modifications (varying model order). Simulation studies are presented to demonstrate the efficacy and practical applicability of the proposed algorithm.
Comments22 pages, 7 figures
