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Sparse Identification of ARX Model Based on <span class="MathJax_Preview">L_{p}</span><script type="math/tex">L_{p}</script>–Regularization | IEEE Conference Publication | IEEE Xplore

Sparse Identification of ARX Model Based on L_{p}–Regularization


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 More

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.
Date of Conference: 25-27 July 2022
Date Added to IEEE Xplore: 11 October 2022
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Conference Location: Hefei, China

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