Kernel Adaptive Filtering Over Complex Networks

This brief is concerned with the problem of kernel adaptive filtering for a complex network. First, a coupled kernel least mean square (KLMS) algorithm is developed for each node to uncover its nonlinear measurement function by using a series of input-output data. Subsequently, an upper bound is der...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 3; S. 1 - 8
Hauptverfasser:	Li, Wenling, Wang, Zidong, Hu, Jun, Du, Junping, Sheng, Weiguo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive filters Adaptive systems Algorithms Complex network Complex networks Convergence Kernel kernel adaptive filter Learning systems least mean square (LMS) Mathematical models recursive least square (RLS) Upper bound Upper bounds
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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