A Class of Convex Optimization-Based Recursive Algorithms for Identification of Stochastic Systems
成果类型：
Article
署名作者：
Ding, Mingxia; Zhao, Wenxiao; Chen, Tianshi; Zhang, Weidong
署名单位：
Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Hainan University; Shanghai Jiao Tong University
刊物名称：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN/ISSBN：
0018-9286
DOI：
10.1109/TAC.2025.3628997
发表日期：
2026
关键词：
摘要：
Focusing on identification, this article develops a class of convex optimization-based criteria and correspondingly the recursive algorithms to estimate the parameter vector theta* of a stochastic dynamic system. Not only do the criteria include the classical least-squares estimator but also the L-l = |center dot |(l), l >= 1, the Huber, the Log-cosh, and the Quantile costs as special cases. First, we prove that the minimizers of the convex optimization-based criteria converge to theta* with probability one. Second, the recursive algorithms are proposed to find the estimates, which minimize the convex optimization-based criteria, and it is shown that these estimates also converge to the true parameter vector with probability one. Numerical examples are given, justifying the performance of the proposed algorithms including the strong consistency of the estimates, the robustness against outliers in the observations, and high efficiency in online computation due to the recursive nature.