In this article, an upper bound is established for the estimation error of a standard least squares (LS) algorithm used to identify a continuous-time model from filtered, sampled input-output data. It is found that the error has three constituent components due to the initial conditions, observation noise, and sample period. In particular, the initial condition bias is bounded by O(1/[NΔt]), which requires sufficiently large [NΔt] for accurate LS estimation. The theoretical results obtained are confirmed by simulation.

中文翻译：

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根据过滤后的采样数据进行连续时间模型识别：误差分析


在本文中，为标准最小二乘 (LS) 算法的估计误差建立了上限，该算法用于从过滤的采样输入输出数据中识别连续时间模型。结果发现，由于初始条件、观测噪声和采样周期，误差具有三个组成部分。特别是，初始条件偏差受 O(1/[NΔt]) 限制，这需要足够大的 [NΔt] 才能进行准确的 LS 估计。获得的理论结果通过模拟得到证实。