ROC performance evaluation of multilayer perceptrons in the detection of one of M orthogonal signals

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Michalopoulou, Z; Nolte, L; Alexandrou, D

Published Date

  • January 1, 1992

Published In

Volume / Issue

  • 2 /

Start / End Page

  • 309 - 312

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 10 (ISBN-10)

  • 0780305329

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.1992.226058

Citation Source

  • Scopus