Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models

• 成果类型: Article
• 署名作者: Chen, Guang-Yong; Gan, Min; Chen, Jing; Chen, Long
• 署名单位: Fuzhou University; Qingdao University; Jiangnan University; University of Macau
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3200950
• 发表日期: 2023
• 页码: 4257-4264
• 关键词: Nonlinear regression models; online identification; Parameter Estimation; variable projection (VP)

摘要

This article presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinear part. In this article, we develop an efficient recursive algorithm for nonlinear regression models based on analyzing the equivalent form of variable projection (VP) algorithm. By introducing the embedded point iteration step, the proposed recursive algorithm can properly exploit the coupling relationship of linear parameters and nonlinear parameters. In addition, we theoretically prove that the proposed algorithm is mean-square bounded. Numerical experiments on synthetic data and real-world time series verify the high efficiency and robustness of the proposed algorithm.