ACCELERATE CONVERGENCE OF POLARIZED RANDOM FOURIER FEATURE-BASED KERNEL ADAPTIVE FILTERING WITH VARIABLE FORGETTING FACTOR AND STEP SIZE

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

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 Stemware random Fourier feature-based kernel least-mean-sq

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Electromagnetic sensor for the control of pipe wall thickness

Calculation of the primary inductive converter with magnetic core is problematic because of the difficulty in Games determining the various fluxes inside and outside the magnetic core.Observance of certain requirements for structural and scheme-related decisions makes is possible to substantially simplify theoretical expressions for description of

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