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Kernel Adaptive Filtering Over Complex Networks
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DOI：10.1109/TNNLS.2022.3199679.png)
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Abstract
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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)
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8.9
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