DocumentCode :
1088517
Title :
Structure and properties of generalized adaptive neural filters for signal enhancement
Author :
Zhang, Zeeman Z. ; Ansari, Nirwan
Author_Institution :
Sci. & Technol. Div., BellSouth Services, Atlanta, GA, USA
Volume :
7
Issue :
4
fYear :
1996
fDate :
7/1/1996 12:00:00 AM
Firstpage :
857
Lastpage :
868
Abstract :
This article addresses the structure and properties of a new class of nonlinear adaptive filters called generalized adaptive neural filters (GANFs). Various properties, such as an upper bound of the mean absolute error of the filters, are analytically derived. Experimental results are presented to demonstrate the performance of the filters for signal and image enhancement. It is shown that GANFs not only extend the class of stack filters, but also have better performance in noise suppression
Keywords :
adaptive filters; image enhancement; neural nets; nonlinear filters; generalized adaptive neural filters; image enhancement; mean absolute error; noise suppression; nonlinear adaptive filters; signal enhancement; stack filters; upper bound; AWGN; Adaptive filters; Additive white noise; Filtering theory; Gaussian noise; Image enhancement; Neural networks; Nonlinear filters; Signal processing; Upper bound;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.508929
Filename :
508929
Link To Document :
بازگشت