Abstract:
This paper studies the sparse identification problem of ARX model by using the L_{p}-regularization method with 0 < p < 1. We construct the information criterion whic...Show MoreMetadata
Abstract:
This paper studies the sparse identification problem of ARX model by using the L_{p}-regularization method with 0 < p < 1. We construct the information criterion which is a linear combination of the prediction error with the L_{p} - regularization term where the coefficient can be adaptively adjusted. By minimizing the information criterion, we propose a sparse parameter identification algorithm based on L_{p}-regularization. We design the adaptive step size by using the observations and obtain the parameter convergence and set convergence of the proposed algorithm. Finally, we give a simulation example to show that the L_{p}-regularization algorithm proposed in this paper has better performance than the L_{1}-regularization algorithm and the recursive least squares.
Published in: 2022 41st Chinese Control Conference (CCC)
Date of Conference: 25-27 July 2022
Date Added to IEEE Xplore: 11 October 2022
ISBN Information: