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A Closed-Form Solution for Kernel Adaptive Filtering
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DOI：10.1016/j.sigpro.2026.110543.png)
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Abstract
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• An extension of Wiener Filter theory to nonlinear systems is presented in an RKHS with a data-dependent kernel function. • The optimal weight function in the RKHS is interpreted as a nonlinear difference equation. • The complexity of the algorithm in the test set is independent of the training set size. The performance is better or on par with other kernel-based nonlinear filtering methods, while offering a more interpretable solution.
Keywords:
Kernel Adaptive Filtering
Nonlinear Systems
Reproducing Kernel Hilbert Space
Optimal Weight Function
Closed-Form Solution
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