A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems
Yanxin Fu1
Wenxiao Zhao2
1.Key Laboratory of Systems and Control,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China2.Key Laboratory of Systems and Control,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China;School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China
摘要：We consider the sparse identification of multivariate ARX systems,i.e.,to recover the zero elements of the unknown parameter matrix.We propose a two-step algorithm,where in the first step the stochastic gradient(SG)algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems.Under mild conditions,we prove that by minimizing the criterion function,the zero elements of the unknown parameter matrix can be recovered with a finite number of observations.The performance of the algorithm is testified through a simulation example.
机标关键词：identificationalgorithmsystemsgradient-basedmultivariatesparsestochastictwo-step
论文发表日期：2024-05-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 213-221 )
英文信息