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

DOI10.1109/TAC.2022.3200950arXiv2207.09958OpenAlexW4312294390MaRDI QIDQ6080224

Author name not available (Why is that?)

Publication date: 2 October 2023

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Abstract: This paper 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 paper, 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 (EPI) 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.

Full work available at URL: https://arxiv.org/abs/2207.09958

zbMATH Keywords

parameter estimationnonlinear regression modelsonline identificationvariable projection (VP)

Mathematics Subject Classification ID

Systems theory; control (93-XX)

Related Items (1)

Variable projection algorithms with sparse constraint for separable nonlinear models

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