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

Changming Cheng; Er-Wei Bai; Zhike Peng

doi:10.1109/tac.2021.3070027

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

IEEE Trans Automat Contr. 2022 Apr;67(4):2027-2032. doi: 10.1109/tac.2021.3070027. Epub 2021 Mar 31.

Authors

Changming Cheng^{1

2}, Er-Wei Bai², Zhike Peng¹

Affiliations

¹ State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai, China.
² Dept. of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242.

Abstract

This paper 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 non-zero coefficient at a time until all non-zero coefficients are found without losing the optimality and the sparsity, thus avoiding the curse of dimensionality often encountered in Volterra system identification.

Keywords: Nonlinear system identification; Orthogonal matching pursuit; Volterra systems.

Abstract

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