This note presents some further results concerning the identification of the nonlinear state-space model (NSSM) based on the meaningful conclusions in the above paper. We use the heavy-tailed Student’s t-distribution to model the system noises and the parameter estimation problem is solved via the expectation maximization (EM) algorithm wherein the decomposition of t-distribution as well as the particle smoother is applied, then a robust identification strategy is proposed. By using the mathematical decomposition of t-distribution, two major advantages are brought: (1) It facilitates the calculation of the desired Q-function efficiently; (2) It allows a more clear and evident explanation of the robustness of the proposed identification strategy.

本说明基于上述论文中有意义的结论，提出了一些关于非线性状态空间模型 (NSSM) 识别的进一步结果。我们使用重尾学生 t 分布对系统噪声进行建模，并通过期望最大化 (EM) 算法解决参数估计问题，其中应用 t 分布分解和粒子平滑器，然后进行鲁棒识别战略提出。通过使用t分布的数学分解，带来了两大优势：（1）它有助于有效地计算所需的Q函数；(2) 它可以更清楚、更明显地解释所提出的识别策略的稳健性。