Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

• المؤلفون الرئيسيون: Azam Khalili, Vahid Vahidpour, Amir Rastegarnia, Ali Farzamnia, Teo, Kenneth Tze Kin, Saeid Sanei
• التنسيق: مقال
• اللغة: English; English
• منشور في: MDPI 2021
• الموضوعات: TA1-2040 Engineering (General). Civil engineering (General)
• الوصول للمادة أونلاين: 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...