Ridge regression and lasso regression based least squares algorithm for a time-delayed rational model via redundant rule
by Zili Zhang; Jing Chen; Yawen Mao
International Journal of Modelling, Identification and Control (IJMIC), Vol. 40, No. 1, 2022

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.

Online publication date: Tue, 12-Jul-2022

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