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

Z. Michalopoulou, L. Nolte, D. Alexandrou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-312
Number of pages4
ISBN (Electronic)0780305329
DOIs
StatePublished - 1992
Externally publishedYes
Event1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 - San Francisco, United States
Duration: Mar 23 1992Mar 26 1992

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
CountryUnited States
CitySan Francisco
Period3/23/923/26/92

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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