DocumentCode :
2630884
Title :
Layered perceptron versus Neyman-Pearson optimal detection
Author :
Bas, Christophe F. ; Marks, Robert J., II
Author_Institution :
IRESTE, Nantes Univ., France
fYear :
1991
fDate :
18-21 Nov 1991
Firstpage :
1486
Abstract :
A layered perceptron artificial neural network (ANN) is trained to detect positive signals corrupted with noise which, for the present test, is Laplacian. Comparison of the ANN performance is made with both Neyman-Pearson optimal and linear detectors. The ANN invariably outperforms the linear detector and is shown to be nearly optimal. The optimal detector requires knowledge of signal and noise parameters; the ANN does not
Keywords :
neural nets; signal detection; Neyman-Pearson optimal detection; artificial neural network; layered perceptron; linear detectors; positive signals; signal detection; Artificial neural networks; Detectors; Gaussian noise; Laplace equations; Neural networks; Neurons; Random variables; Signal design; Signal detection; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
Type :
conf
DOI :
10.1109/IJCNN.1991.170610
Filename :
170610
Link To Document :
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