TY - GEN
T1 - ROC performance evaluation of multilayer perceptrons in the detection of one of M orthogonal signals
AU - Michalopoulou, Z.
AU - Nolte, L.
AU - Alexandrou, D.
N1 - Publisher Copyright:
© 1992 IEEE.
PY - 1992
Y1 - 1992
N2 - A neural network detector is compared to an optimal algorithm from signal detection theory for the problem of one of M orthogonal signals in a Gaussian noise environment. The neural detector is a multilayer per-ceptron trained with the back-propagation algorithm, while the optimal detector operates based on a likelihood ratio test. It was observed that for the signal-known-exactly case (M = 1) the performance of the neural detector converges to the performance of the ideal Bayesian decision processor; however, for a higher degree of uncertainty (i.e. for a larger M) the performance of the multilayer perceptron is obviously inferior to that of the optimal detector. In addition, it was concluded that noise information in the training stage affects only slightly the performance of the neural detector. However, the knowledge of the noise distribution proved to be vital for the detection theory processor.
AB - A neural network detector is compared to an optimal algorithm from signal detection theory for the problem of one of M orthogonal signals in a Gaussian noise environment. The neural detector is a multilayer per-ceptron trained with the back-propagation algorithm, while the optimal detector operates based on a likelihood ratio test. It was observed that for the signal-known-exactly case (M = 1) the performance of the neural detector converges to the performance of the ideal Bayesian decision processor; however, for a higher degree of uncertainty (i.e. for a larger M) the performance of the multilayer perceptron is obviously inferior to that of the optimal detector. In addition, it was concluded that noise information in the training stage affects only slightly the performance of the neural detector. However, the knowledge of the noise distribution proved to be vital for the detection theory processor.
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U2 - 10.1109/ICASSP.1992.226058
DO - 10.1109/ICASSP.1992.226058
M3 - Conference contribution
AN - SCOPUS:33747632245
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 309
EP - 312
BT - ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
Y2 - 23 March 1992 through 26 March 1992
ER -