Smoothed least mean p-power error criterion for adaptive filtering.

In this paper, we propose a novel error criterion for adaptive filtering, namely the smoothed least mean p -power (SLMP) error criterion, which aims to minimize the mean p -power of the error plus an independent and scaled smoothing variable . Some important properties of the SLMP criterion are presented. In particular, we show that if the smoothing variable is symmetric and zero-mean, and p is an even number, then the SLMP error criterion will become a weighted sum of the even-order moments of the error, and as the smoothing factor (i.e. the scale factor) is large enough, this new criterion will be approximately equivalent to the well-known mean square error (MSE) criterion. Based on the proposed error criterion, we develop a new adaptive filtering algorithm and its kernelized version, and derive a theoretical value of the steady-state excess mean square error (EMSE). Simulation results suggest that the new algorithms with proper choice of the smoothing factor may perform quite well. [ABSTRACT FROM AUTHOR]