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Issue:Generalized net model of forest fire detection with ART2 neural network

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Title of paper: Generalized net model of forest fire detection with ART2 neural network
Author(s):
Todor Petkov
"Prof. Asen Zlatarov" University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
todor_petkov@btu.bg
Stanimir Surchev
Prof. Asen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
stanimir_surchev@btu.bg
Sotir Sotirov
Prof. Asen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
ssotirov@btu.bg
Presented at: 14th IWGN, Burgas, 29-30 November 2013
Published in: Conference proceedings, pages 28-33
Download: Download-icon.png PDF (158  Kb, Info)
Abstract: This paper thoroughly describes the use of unsupervised adaptive resonance theory ART2 neural network for the purposes of forest fire detection. In order to train the network, the pixel value of red color is regarded as learning vector. At the end the trained network was

tested by the values of a picture and determines the design, or how to visualize the converted picture. As a result we had the same picture with colors according to the network. Here we use the generalized net to prepare a model that describes the process of the color recognition.

Keywords: Generalized Nets, Neural Networks, Adaptive Resonance Theory
AMS Classification: 68Q85.
References:
  1. Atanassov, K., Generalized Nets, World Scientific, Singapore, 1991.
  2. Carpenter, G. A., S. Grossberg. The ART of adaptive pattern recognition by a self-organizing neural network, Computer, Vol. 21, March 1988, No. 3, 77−88.
  3. Grossberg, G., A. Carpenter ART2: Self-organization of stable category recognition codes of analog input patterns, Boston University, Center for Adaptive systems, 1987 Dec 1; 26(23), 4919–4930.
  4. Grossberg, G. Adaptive pattern classification and universal recoding. II. Feedback,expectation, olfacation, and illusions, Bioi. Cybemet. Vol. 23, 1976, 187–202.
  5. Sivanandam, S. N., S. N. Deepa, Introduction to Neural Networks using Matlab 6.0.
  6. Krose B., P. van der Smagt, An introduction to Neural Networks, Chapter 6. 8th Ed., University of Amsterdam, 1996.
  7. Fausett, L. Fundamentals of Neural Networks; Architecture, algorithms and applications, Prentice Hall, 1993.
  8. McCulloch, W., W. Pitts, A logical calculus of the ideasimmanent in nervous activity. Bulletin of Mathematical Biophysics, Vol. 5, 1943, No. 4, 115−133.
  9. http://en.wikipedia.org/wiki/File:Mount_Carmel_forest_fire14.jpg
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