Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

• 主要な著者: Azam Khalili, Vahid Vahidpour, Amir Rastegarnia, Ali Farzamnia, Teo, Kenneth Tze Kin, Saeid Sanei
• フォーマット: 論文
• 言語: English; English
• 出版事項: MDPI 2021
• 主題: TA1-2040 Engineering (General). Civil engineering (General)
• オンライン･アクセス: https://eprints.ums.edu.my/id/eprint/32939/1/Coordinate-Descent%20Adaptation%20over%20Hamiltonian%20Multi-Agent%20Networks.pdf; https://eprints.ums.edu.my/id/eprint/32939/2/Coordinate-Descent%20Adaptation%20over%20Hamiltonian%20Multi-Agent%20Networks1.pdf; https://eprints.ums.edu.my/id/eprint/32939/; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621694/pdf/sensors-21-07732.pdf; https://doi.org/10.3390/s21227732

The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. Howeve...