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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Summary: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 derived for the step-size of the coupled KLMS algorithm to guarantee the mean square convergence. It is shown that the upper bound is dependent on the coupling weights of the complex network. Especially, an optimal step size is obtained to achieve the fastest convergence speed and a suboptimal step size is presented for the purpose of practical implementations. Besides, a coupled kernel recursive least square (KRLS) algorithm is further proposed to improve the filtering performance. Finally, simulations are provided to verify the validity of the theoretical results.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3199679