Adaptive neural filters

Abstract

The authors introduce a new class of nonlinear filters called neural filters based on the threshold decomposition and neural networks. Neural filters can approximate both linear FIR filters and weighted order statistic (WOS) filters which include median, rank order, and weighted median filters. An adaptive algorithm is derived for determining optimal neural filters under the mean squared error (MSE) criterion. Experimental results demonstrate that if the input signal is corrupted by Gaussian noise adaptive neural filters converge to linear filters and if corrupted by impulsive noise, optimal neural filters become WOS filters.<>