Journal article
Identification of Sparse Volterra Systems
IEEE transactions on automatic control, Vol.67(4), pp.2027-2032
03/31/2021
DOI: 10.1109/TAC.2021.3070027
PMID: 35480236
Abstract
This paper considers identification of sparse Volterra systems. Two identification meth- ods based on the almost orthogonal matching pur- suit (AOMP) and the RIVAL (removing irrele- vant variables amidst Lasso iterations) algorithm are 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 sys- tem identification. However, the conditions for the AOMP are strong. To this end, the RIVAL is pro- posed that solves the set identification and param- eter estimation problems simultaneously with the convergence results, and outperforms the standard Lasso-type algorithms.
Details
- Title: Subtitle
- Identification of Sparse Volterra Systems
- Creators
- Changming Cheng - Shanghai Jiao Tong UniversityEr-Wei Bai - University of IowaZhike Peng - Shanghai Jiao Tong University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on automatic control, Vol.67(4), pp.2027-2032
- DOI
- 10.1109/TAC.2021.3070027
- PMID
- 35480236
- NLM abbreviation
- IEEE Trans Automat Contr
- ISSN
- 0018-9286
- eISSN
- 1558-2523
- Publisher
- IEEE
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: R42CA195819, R15AG061755; DOI: 10.13039/501100008982, name: National Science Foundation, award: CNS-1239509; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 11632011, 11702171, 11872243, 51121063
- Language
- English
- Date published
- 03/31/2021
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197068802771
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