Title :
Noise density estimation using neural networks
Author :
Musavi, M.T. ; Hummels, D.M. ; Laffely, A.J. ; Kennedy, S.P.
Author_Institution :
Maine Univ., Orono, ME, USA
fDate :
31 Aug-2 Sep 1992
Abstract :
A neural network for estimation of unknown noise densities and their gradients is presented. The network structure is similar to a radial basis function. The learning rule is, however, different and has an unsupervised nature that ensures a valid probability density. The algorithm is fast and provides good estimates of noise densities. One and two dimensional examples are reported
Keywords :
neural nets; noise; signal processing; algorithm; gradients; learning rule; network structure; neural networks; noise density estimation; probability density; radial basis function; signal processing; Computer networks; Covariance matrix; Electronic mail; Kernel; Neural networks; Radial basis function networks; Signal detection; Signal processing algorithms; Testing; Working environment noise;
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
DOI :
10.1109/NNSP.1992.253664
