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Learning neural networks for detection and classification of synchronous recurrent transient signals

Publication ,  Journal Article
Gelenbe, E; Harmanci, K; Krolik, J
Published in: Signal Processing
February 26, 1998

This paper proposes a neural network solution to the classical signal processing problem of detection of a synchronous recurrent transient signal in noise. If a signal exists, it is assumed to be one of M known signals which may sometimes occur (probabilistically) in successive intervals. Several neural network configurations are applied to this problem and compared with each other and with the optimum adaptive sequential detector. A novel efficient neural network detector is proposed using an XOR-Tree configuration with learning. Tests with synthetic and real noise, show the excellent performance of this approach as compared to the optimum adaptive detector and to other neural network techniques. With real (non-white) noise obtained from sonar data, the XOR-Tree network widely outperforms the likelihood ratio detector. We also discuss the learning time complexity of the XOR-Tree network and compare it to that of standard three layer network architectures. © 1998 Elsevier Science B.V. All rights reserved.

Duke Scholars

Published In

Signal Processing

DOI

ISSN

0165-1684

Publication Date

February 26, 1998

Volume

64

Issue

3

Start / End Page

233 / 247

Related Subject Headings

  • Networking & Telecommunications
  • 46 Information and computing sciences
  • 40 Engineering
  • 10 Technology
  • 09 Engineering
  • 08 Information and Computing Sciences
 

Citation

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Gelenbe, E., Harmanci, K., & Krolik, J. (1998). Learning neural networks for detection and classification of synchronous recurrent transient signals. Signal Processing, 64(3), 233–247. https://doi.org/10.1016/S0165-1684(97)00193-X
Gelenbe, E., K. Harmanci, and J. Krolik. “Learning neural networks for detection and classification of synchronous recurrent transient signals.” Signal Processing 64, no. 3 (February 26, 1998): 233–47. https://doi.org/10.1016/S0165-1684(97)00193-X.
Gelenbe E, Harmanci K, Krolik J. Learning neural networks for detection and classification of synchronous recurrent transient signals. Signal Processing. 1998 Feb 26;64(3):233–47.
Gelenbe, E., et al. “Learning neural networks for detection and classification of synchronous recurrent transient signals.” Signal Processing, vol. 64, no. 3, Feb. 1998, pp. 233–47. Scopus, doi:10.1016/S0165-1684(97)00193-X.
Gelenbe E, Harmanci K, Krolik J. Learning neural networks for detection and classification of synchronous recurrent transient signals. Signal Processing. 1998 Feb 26;64(3):233–247.
Journal cover image

Published In

Signal Processing

DOI

ISSN

0165-1684

Publication Date

February 26, 1998

Volume

64

Issue

3

Start / End Page

233 / 247

Related Subject Headings

  • Networking & Telecommunications
  • 46 Information and computing sciences
  • 40 Engineering
  • 10 Technology
  • 09 Engineering
  • 08 Information and Computing Sciences