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Subsystem Interaction-Driven Distributed Adaptive Clustering and Estimation Over Multitask Networks

delete2026-06-17
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PRE
AI
L
Luyao Xu
M
Miaomiao Xu
F
Feng Chen
胡利梅 封面图
胡利梅 (Limei Hu)
M
Minyu Feng
王华敏 封面图
王华敏 (Huamin Wang)
DOI：10.1109/taes.2026.3702640delete
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摘要

摘要

En 中文
分布式多任务估计在无线传感器网络中得到了广泛研究。然而，实际场景中的未知聚类信息和复杂网络条件导致估计性能下降。为解决该问题，我们构建了一个由非合作子系统与扩散子系统组成的混合系统。在此基础上，提出了一种基于子系统信息交互驱动的鲁棒分布式聚类与估计算法，其中设计了子系统间的信息交互机制以提高聚类精度。随后，分析了所提算法在均值和均方意义下的收敛行为。此外，基于交互系数开发了一种自适应学习策略，进一步优化了各子系统的性能。基于此，提出了一种基于子系统学习交互驱动的分布式自适应聚类与估计算法。最后，仿真结果验证了所提算法相较于相关算法的有效性与鲁棒性。
Keyword:
Adaptive filter
clustering
distributed estimation
multitask learning
wireless sensor networks (WSNs)

期刊

IEEE Transactions on Aerospace and Electronic Systems 封面图
IEEE Transactions on Aerospace and Electronic Systems
IF:
5.7
论文数:
780
被引数:
2.4W

机构

S
southwest university
学者数:
5.4K
论文数: 1.7K
被引数: 0
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