Title :
Adaptive neural filters
Author :
Yin, Lin ; Astola, Jaakko ; Neuvo, Yrjö
Author_Institution :
Dept. of Electr. Eng., Tampere Univ. of Technol., Finland
fDate :
30 Sep-1 Oct 1991
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
Keywords :
adaptive filters; neural nets; Gaussian noise; adaptive algorithm; mean squared error; neural filters; neural networks; nonlinear filters; optimal neural filters; threshold decomposition; weighted order statistic filters; Adaptive algorithm; Adaptive filters; Adaptive systems; Finite impulse response filter; Gaussian noise; Neural networks; Neurons; Noise cancellation; Nonlinear filters; Signal processing algorithms;
Conference_Titel :
Neural Networks for Signal Processing [1991]., Proceedings of the 1991 IEEE Workshop
Conference_Location :
Princeton, NJ
Print_ISBN :
0-7803-0118-8
DOI :
10.1109/NNSP.1991.239491
