Identification of Sparse Volterra Systems: An Almost Orthogonal Matching Pursuit Approach

• 成果类型: Article
• 署名作者: Cheng, Changming; Bai, Er-Wei; Peng, Zhike
• 署名单位: University of Iowa
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3070027
• 发表日期: 2022
• 页码: 2027-2032
• 关键词: Matching pursuit algorithms; kernel; estimation; Sparse matrices; Nonlinear systems; STANDARDS; Mathematical model; nonlinear system identification; orthogonal matching pursuit (OMP); Volterra systems

摘要

This article considers identification of sparse Volterra systems. A method based on the almost orthogonal matching pursuit (AOMP) is proposed. The AOMP algorithm allows one to estimate one nonzero coefficient at a time until all nonzero coefficients are found without losing the optimality and the sparsity, thus avoiding the curse of dimensionality often encountered in Volterra system identification.