Title :
System identification and noise cancellation via neural-net computing
Author :
Park, Gwang-Hoon ; Pao, Yoh-Han
Author_Institution :
Dept. of Electr. Eng. & Appl. Phys., Case Western Reserve Univ., Cleveland, OH, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
We report on highly favorable results obtained in use of neural-net computing in the learning of processes and in the cancellation of noise in signals obscured by noise. In the first instance, we demonstrate the ability to accurately learn models of linear and nonlinear functional mappings in noisy environments. In the case of noise cancellation, we report on the ability to extract a signal from noisy background
Keywords :
identification; interference suppression; neural nets; signal processing; linear functional mappings; model learning; neural net computing; noise cancellation; nonlinear functional mapping; signal extraction; system identification; Adaptive algorithm; Neural networks; Noise cancellation; Nonlinear systems; Physics; Signal processing; Signal processing algorithms; System identification; Vectors; Working environment noise;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.375038
