A Globally Stable LPNN Model for Sparse Approximation
- DOI
- 10.1109/tnnls.2021.3126730
- Published
- 2023-08
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2021.3126730,
title = {A Globally Stable LPNN Model for Sparse Approximation},
author = {Hao Wang and Ruibin Feng and Chi-Sing Leung and John Sum and Anthony G. Constantinides},
year = {2023},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2021.3126730},
url = {https://doi.org/10.1109/tnnls.2021.3126730}
}RIS
TY - JOUR TI - A Globally Stable LPNN Model for Sparse Approximation AU - Hao Wang AU - Ruibin Feng AU - Chi-Sing Leung AU - John Sum AU - Anthony G. Constantinides PY - 2023 JO - IEEE Transactions on Neural Networks and Learning Systems DO - 10.1109/tnnls.2021.3126730 UR - https://doi.org/10.1109/tnnls.2021.3126730 ER -
APA
Wang, H., Feng, R., Leung, C., Sum, J., & Constantinides, A. G. (2023). A Globally Stable LPNN Model for Sparse Approximation. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2021.3126730
Source records
- crossref · retrieved 2026-09-25T14:19:01.920Z