Further results on "System identification of nonlinear state-space models"

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. (c) 2022 Elsevier Ltd. All rights reserved.