Title :
Noise cancellation in time and frequency domain using neural networks
Author :
Rahman, M.T. ; Lebby, G.L. ; Sherrod, E.E.
Author_Institution :
Machine Intelligence & Power Associated Res. Lab., North Carolina A&T State Univ., Greensboro, NC, USA
Abstract :
Artificial neural networks can be used effectively to filter out noise from frequency shift keying (FSK) and phase shift keying (PSK) modulation signals corrupted with random noise. In the present paper, the time and frequency domain filtering schemes are investigated. The number of data points are optimized using a method described as selective truncation. In order to evaluate the performance of both the time and frequency domain filters, a series of tests is conducted using test signals. The training network parameters are optimized in order to speed up convergence
Keywords :
adaptive filters; convergence; filtering and prediction theory; frequency shift keying; frequency-domain analysis; interference suppression; learning (artificial intelligence); neural nets; optimisation; phase shift keying; random noise; time-domain analysis; FSK; PSK; artificial neural networks; convergence; data points numbers; frequency domain filtering schemes; frequency shift keying; performance; phase shift keying; random noise; selective truncation; test signals; time domain filtering schemes; training network parameters; Artificial neural networks; Filtering; Filters; Frequency domain analysis; Frequency shift keying; Noise cancellation; Phase modulation; Phase noise; Phase shift keying; Testing;
Conference_Titel :
System Theory, 1994., Proceedings of the 26th Southeastern Symposium on
Conference_Location :
Athens, OH
Print_ISBN :
0-8186-5320-5
DOI :
10.1109/SSST.1994.287800
