@inproceedings{74b361edb85b4d1bb0f6f137692a9577,
title = "Cooperative PSO Heuristic for System Identification beyond MSE Boundary in Ad-Hoc Network",
abstract = "Cooperative topology of Participle Swarm Optimization (PSO) heuristic (CPSO) is implemented for system identification over ad-hoc Networks. The main achievement is the development of PSO heuristic for system identification instead of the conventional diffusion topology of the Least Mean Square called DLMS algorithm which reveals a significant improvement in terms of the Mean Square Error (MSE). The CPSO heuristic demonstrates an important MSE convergence result beyond the theoretical boundary of noise variance. In the aggregation stage, each sensor exchanges the local best objective function along with the related local best estimate to near-by neighboring sensors. The simulations indicate that the developed CPSO approach achieves extraordinary MSE and MSD improvements against existing DLMS algorithms when the input block size and population are increased.",
keywords = "ad-hoc networks, channel identification, diffusion least mean square (dlms), mean square error (mse), participle swarm optimization (pso)",
author = "Almohammedi, \{Ali M.\} and Mohamed Deriche",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 ; Conference date: 20-02-2023 Through 23-02-2023",
year = "2023",
doi = "10.1109/SSD58187.2023.10411280",
language = "English",
series = "2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "580--585",
booktitle = "2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023",
address = "United States",
}