Exponentially Weighted Kernel Recursive Least P-Power Algorithm

This paper presents a novel exponentially weighted kernel recursive least p-power (EW-KRLP) algorithm. The proposed algorithm is derived exploiting the cost function of recursive weighted least p-power error instead of widely used second-order statistical measure of error, achieving the good tracking ability of non-stationarity and the superior robustness in the presence of impulsive noise. Simulations demonstrate the EW-KRLP algorithm has better convergence performance than the existing kernel adaptive filtering approaches in identifying the non-stationary nonlinear system under the assumption of non-Gaussian impulsive noise modeled by the symmetric α-stable distribution.

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