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Title: Ridge regression and lasso regression based least squares algorithm for a time-delayed rational model via redundant rule

Authors: Zili Zhang; Jing Chen; Yawen Mao

Addresses: School of Science, Jiangnan University, Wuxi 214122, China ' School of Science, Jiangnan University, Wuxi 214122, China ' School of Science, Jiangnan University, Wuxi 214122, China

Abstract: This paper proposes a ridge regression based least squares algorithm (LS-RR) and a three stage lasso regression based least squares algorithm (TS-LS-LR) for a rational model with unknown time-delay. By using redundant rule method, the time-delayed rational model is turned into a new model. In order to identify the parameters of this new model, the LS-RR algorithm is proposed. The parameter vector of this model contains two parts, redundant parameters and true parameters. To pick out the redundant parameters, the lasso regression based least squares algorithm (LS-LR) is proposed. Furthermore, the TS-LS-LR is introduced to improve the estimation accuracy. The numerical simulation shows the effectiveness of the proposed algorithms.

Keywords: polynomial nonlinear system; time-delayed model; rational model; lasso regression; ridge regression; redundant rule.

DOI: 10.1504/IJMIC.2022.10048798

International Journal of Modelling, Identification and Control, 2022 Vol.40 No.1, pp.11 - 17

Received: 03 Jun 2021
Accepted: 07 Aug 2021

Published online: 12 Jul 2022 *

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