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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Bibliographic Details

Published in:	IEEE transaction on neural networks and learning systems Vol. 35; no. 3; pp. 1 - 8
Main Authors:	Li, Wenling, Wang, Zidong, Hu, Jun, Du, Junping, Sheng, Weiguo
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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