Cheng Changming, Bai Er-Wei, Peng Zhike
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai, China.
Dept. of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242.
IEEE Trans Automat Contr. 2022 Apr;67(4):2027-2032. doi: 10.1109/tac.2021.3070027. Epub 2021 Mar 31.
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.
本文考虑稀疏Volterra系统的辨识问题。提出了一种基于近似正交匹配追踪（AOMP）的方法。AOMP算法允许每次估计一个非零系数，直到找到所有非零系数，同时不损失最优性和稀疏性，从而避免了Volterra系统辨识中经常遇到的维数灾难。
