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