Issue:Intuitionistic fuzzy Multilayer Perceptron as a part of integrated systems for early forest-fire detection

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http://ifigenia.org/wiki/issue:nifs/19/3/81-89

Title of paper: Intuitionistic fuzzy Multilayer Perceptron as a part of integrated systems for early forest-fire detection Author(s): Sotir Sotirov Prof. Asen Zlatarov University, “Prof. Yakimov” Blvd., Bourgas 8000, Bulgaria ssotirov@btu.bg Ivelina Vardeva Prof. Asen Zlatarov University, “Prof. Yakimov” Blvd., Bourgas 8000, Bulgaria ivardeva@gmail.com Maciej Krawczak Higher School of Applied Informatics and Management, Newelska 6, 01-447 Warsaw, Poland krawczak@ibspan.waw.pl Presented at: 17th International Conference on Intuitionistic Fuzzy Sets, 1–2 November 2013, Sofia, Bulgaria Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 19, 2013, Number 3, pages 81—89 Download:  PDF (294  Kb, File info) Abstract: In this paper we present intuitionistic fuzzy neural network as a part of generalized net model of multi-sensorial integrated systems for early detection of forest fires. Many information and data sources have been used, including infrared images, visual images, sensors data, and geographic data bases. One of the main purpose is using of the intelligent methods for decision when must alarm starts. Here we use intuitionistic fuzzy neural networks, as a one of the possibilities of intelligent systems. Keywords: Intuitionistic fuzzy set, Index Matrix, Modelling, Neural network. AMS Classification: 03E72 References: Angayarkkani, K., N. Radhakrishnan, An Intelligent System For Effective Forest Fire Detection Using Spatial Data, International Journal of Computer Science and Information Security, Vol. 7, 2010, No. 1, 202–208. Antonov, A. Generalized net model for parallel training of hidden units in neural networks with radial basis functions, Comptes rendus de l'Academie bulgare des Sciences, Vol. 66, 2013, No. 9, 1239–1246. Atanassov, K. Generalized Nets, World Scientific, Singapore, 1991. Atanassov, K. On Generalized Nets Theory, “Prof. M. Drinov” Academic Publishing House, Sofia, 2007. Atanassov, K. On Intuitionistic Fuzzy Sets Theory. Studies in fuzziness and soft computing, Springer Physica-Verlag, Berlin, 2012. Atanassov, K., S. Sotirov, A. Antonov, Generalized net model for parallel optimization of feed-forward neural network, Advanced Studies in Contemporary Mathematics,Vol.15, No. 1, 2007, 109–119. Commission internationale de l'Eclairage proceedings, 1931. Cambridge: Cambridge University Press. Hagan, M., H. Demuth, M. Beale, Neural Network Design, PWS Publishing, Boston, MA, 1996. Haykin, S. Neural Networks: A Comprehensive Foundation, NY: Macmillan, 1994. Krawczak, M. Generalized Net Models of Systems, Bulletin of Polish Academy of Science, 2003. Haykin, S. Neural Networks: A Comprehensive Foundation, NY: Macmillan, 1994. Rumelhart, D., G. Hinton, R. Williams. Training representation by back-propagation errors, Nature, Vol. 323, 1986, 533–536. Sotirov, S. A method of accelerating neural network training, Neural Processing Letters, Springer Science+Business Media B.V., Formerly Kluwer Academic Publishers B.V., Vol. 22, Oct 2005, Issue 2, 163–169. Sotirov, S. Modeling the algorithm Backpropagation for training of neural networks with generalized nets. Part 1, Proceedings of the 4th International Workshop on Generalized Nets, Sofia, 23 September 2003, 61–67. Sotirov, S., K. Atanassov, Intuitionistic Fuzzy Feed Forward Neural Network. Part 1, Cybernetics and Information Technologies, Vol. 2, 2009, 62–68. Sotirov, S., M. Krawczak. Modeling the algorithm Backpropagation for training of neural networks with generalized nets. Part 2, Issues in Intuitionistic Fuzzy Sets and Generalized nets, Warsaw, 2003, 65–70. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.