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Accelerate Convergence of Polarized Random Fourier Feature-Based Kernel Adaptive Filtering With Variable Forgetting Factor and Step Size

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The random Fourier feature as an efficient kernel approximation method can effectively suppress the network growth of the traditional kernel-based adaptive filtering algorithm. Polarized random Fourier feature kernel least-mean-square(PRFFKLMS) remarkably improved the accuracy performance of random Fourier feature-based kernel least-mean-square algorithm and become the most representa... https://www.learningepistemology.com/product-category/brow-growth-treatments/
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