Kernel Adaptive Filtering Over Complex Networks

doi:10.1109/TNNLS.2022.3199679

Kernel Adaptive Filtering Over Complex Networks

2024-03-01

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DOI：10.1109/TNNLS.2022.3199679

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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.

Keywords:

Kernel

Complex networks

Convergence

Adaptive systems

Upper bound

Learning systems

Mathematical models

Complex network

kernel adaptive filter

least mean square (LMS)

recursive least square (RLS)

Kernel Adaptive Filtering Over Complex Networks

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