Odor detection using pulse coupled neural networks

Geza Szekely, Mary Lou Padgett, Gerry Dozier, T. A. Roppel

Research output: Contribution to conferencePaper

4 Citations (Scopus)


Based on neural structure (not physiology) observed in clinical experiments, an odor image can be constructed for analysis with a cutting-edge image processing procedure termed Pulse Coupled Neural Networks Factoring (PCNNf). Enhancement of an odor image using PCNNf can significantly increase detection accuracy. Selection of the proper parameters for the implementation usually requires analysis by an expert familiar with the application targeted. Once suitable parameters have been selected, the PCNNf procedure is very robust, and can typically be used in a large number of situations similar to the original application. The purpose of this research is to advance the methodology for selecting parameters with reduced input from experts. The approach selected is use of a set of evolutionary algorithms (EAs) to find improved parameter sets and to establish automated procedures for setting bounds on parameters and Weight matrices for particular applications.

Original languageEnglish
Number of pages5
Publication statusPublished - Dec 1 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Szekely, G., Padgett, M. L., Dozier, G., & Roppel, T. A. (1999). Odor detection using pulse coupled neural networks. 317-321. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .