Araştırma Makalesi
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Geçmişten Günümüze Yapay Sinir Ağları ve Tarihçesi

Yıl 2017, Cilt: 5 Sayı: 2, 8 - 18, 29.12.2017

Öz

Yapay sinir ağlarının günümüzde birçok alanda kullanımına rastlamak
mümkündür. Yapay sinir ağları, birden fazla nöronun belirli disiplin
çerçevesinde bir araya getirilmesiyle bir görevin gerçekleştirilmesi için
yapısal, istatistiksel, matematiksel ve felsefi sorunlara çözüm üreten bir bilim
dalıdır. Çalışmada yapay sinir ağlarının geçmişten günümüze kadar olan gelişme
süreci ve tarihi ele alınmıştır. Ortaya çıktığı ilk günden günümüze kadar
gelişim süreci üzerinde durulmuş ve aşama aşama kronolojik olarak elde ettiği
değişimler irdelenmiştir.

Kaynakça

  • Anderson, J. A. (1972). A simple neural network generating on interactive memory. Mathematical Biosciences, 14, 197-220.
  • Broomhead, D. S., & Lowe, D. (1988). Radial basis-functions, multi-variable functional interpolation and adaptive networks. Royal signals and radar establishment memorandum, 41-48.
  • Caianiello, E. R. (1961). Outline of a theory of thought-processes and thinking machines. Journal of Theoretical Biology, 2, 204-235.
  • Efe, M. Ö., Abadoğlu, E., & Kaynak, O. (1999). Analysis and Desing of a Neural Network Assisted Nonlinear Controller for a Bioreactor. International Journal of Robust and Nonlinear Control, 9(11), 799-815.
  • Efe, M. Ö., & Kaynak, O. (1999). A Comparative Study of Neural Network Structures in Indentification of Nonlinear Systems. Mechatronics, 9(3), 287-300.
  • Efe, Ö., & Kaynak, O. (2000). Yapay Sinir Ağları ve Uygulamaları: Boğaziçi Üniversitesi.
  • Elmas, Ç. (2007). Yapay Zeka Uygulamaları: Seçkin.
  • Farely, B. G., & Clark, W. A. (1954). Simulation of self-organizing systems by digital computers. IEEE Transactions of Professional Group of Information Theory, PGIT-4, 76-84.
  • Fukushima, K. (1986). A neural network model for selective attention in visual pattern recognition. Biol. Cybernetics, 55, 5-15.
  • Fukushima, K., Miyake, S., & Ito, T. (1983). Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man and Cybernetics, SMC-13.
  • Hebb, D. O. (1949). The organization of behaviour. The first stage of perception: growth of the assembly, 4, 60-78.
  • Hopfield, J. J. (1982a). Neural networks and physicalsystems with emergent collective computational abilities. Proceedings of the National, Academy of Sciences, 79, 2554-2558.
  • Hopfield, J. J. (1982b). Neurons with graded response have collective computational properties like those of two state neurons. Proceedings of the National, Academy of Sciences, 81, 3088-3092.
  • Irwin, G. W., Warwick, K., & Hunt, K. J. (1995). Neural Network Applications in Control. United Kingdom: The Instution of Electrical Engineers. James, W. (1890). Psychology (Briefer Course). Association, 16, 253-279.
  • Kohonen, T. (1972). Correlation matrix memories. IEEE Transactions on Computers, 21(4), 353-359.
  • Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.
  • McClelland, J. L., & Rumelhart, D. E. (1986). Parallel distributed processing, explorations in the microstructure of cognition. Psychological and biological models, MIT Press, Cambridge, MA, 2.
  • McCulloch, W. S., & Pitts, W. A. (1943). A logical calculus of the ideas immanent in nervous activity. Buttetin of Mathematics and Biophysics, 5, 115-133.
  • Minsky, M., & Papert, S. (1969). Perceptrons. MIT Press, Cambridge, MA.
  • Narendra, K. S., & Parthasarathy, K. (1990). Indentification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
  • Nilson, N. J. (1965). Learning Machines. McGraw-Hill.
  • Öztemel, E. (2012). Yapay Sinir Ağları (Vol. 3). İstanbul: Papatya Yayıncılık Eğitim.
  • Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychoanalytic Review, 65, 386-408.
  • Rumelhart, D. E., Hinton, D. E., & Williams, R. J. (1986). Learning representation by backpropagating errors. Nature, 323(9), 533-536.
  • Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing, explorations in the microstructure of cognition. Foundations, MIT Press Cambridge, MA, 1.
  • Rumelhart, D. E., & McClelland, J. L. (1988). Parallel distributed processing, explorations in the microstructure of cognition, A handbook of models, programs and exercies. MIT Press, Cambridge, MA.
  • Specht, D. F. (1988). Probabilistic neural networks for classification, mapping or associative memory. IEEE Conference on Neural Networks, 1, 525-532.
  • Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568-576.
  • Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. WESTCON Convention, Record Part IV, 96-104.

From Past to Present Artificial Neural Networks and History

Yıl 2017, Cilt: 5 Sayı: 2, 8 - 18, 29.12.2017

Öz

It is possible to find artificial neural networks in
many places today. Artificial neural networks are a science that produces
solutions to structural, statistical, mathematical, and philosophical problems to
accomplish a task by bringing multiple neurons together with rules. The
development process and history of the artificial neural networks from the past
to the present day are discussed in the study. The developmental process has
been focused from the first day up to the present day, and the chronological
changes have been examined gradually.

Kaynakça

  • Anderson, J. A. (1972). A simple neural network generating on interactive memory. Mathematical Biosciences, 14, 197-220.
  • Broomhead, D. S., & Lowe, D. (1988). Radial basis-functions, multi-variable functional interpolation and adaptive networks. Royal signals and radar establishment memorandum, 41-48.
  • Caianiello, E. R. (1961). Outline of a theory of thought-processes and thinking machines. Journal of Theoretical Biology, 2, 204-235.
  • Efe, M. Ö., Abadoğlu, E., & Kaynak, O. (1999). Analysis and Desing of a Neural Network Assisted Nonlinear Controller for a Bioreactor. International Journal of Robust and Nonlinear Control, 9(11), 799-815.
  • Efe, M. Ö., & Kaynak, O. (1999). A Comparative Study of Neural Network Structures in Indentification of Nonlinear Systems. Mechatronics, 9(3), 287-300.
  • Efe, Ö., & Kaynak, O. (2000). Yapay Sinir Ağları ve Uygulamaları: Boğaziçi Üniversitesi.
  • Elmas, Ç. (2007). Yapay Zeka Uygulamaları: Seçkin.
  • Farely, B. G., & Clark, W. A. (1954). Simulation of self-organizing systems by digital computers. IEEE Transactions of Professional Group of Information Theory, PGIT-4, 76-84.
  • Fukushima, K. (1986). A neural network model for selective attention in visual pattern recognition. Biol. Cybernetics, 55, 5-15.
  • Fukushima, K., Miyake, S., & Ito, T. (1983). Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man and Cybernetics, SMC-13.
  • Hebb, D. O. (1949). The organization of behaviour. The first stage of perception: growth of the assembly, 4, 60-78.
  • Hopfield, J. J. (1982a). Neural networks and physicalsystems with emergent collective computational abilities. Proceedings of the National, Academy of Sciences, 79, 2554-2558.
  • Hopfield, J. J. (1982b). Neurons with graded response have collective computational properties like those of two state neurons. Proceedings of the National, Academy of Sciences, 81, 3088-3092.
  • Irwin, G. W., Warwick, K., & Hunt, K. J. (1995). Neural Network Applications in Control. United Kingdom: The Instution of Electrical Engineers. James, W. (1890). Psychology (Briefer Course). Association, 16, 253-279.
  • Kohonen, T. (1972). Correlation matrix memories. IEEE Transactions on Computers, 21(4), 353-359.
  • Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.
  • McClelland, J. L., & Rumelhart, D. E. (1986). Parallel distributed processing, explorations in the microstructure of cognition. Psychological and biological models, MIT Press, Cambridge, MA, 2.
  • McCulloch, W. S., & Pitts, W. A. (1943). A logical calculus of the ideas immanent in nervous activity. Buttetin of Mathematics and Biophysics, 5, 115-133.
  • Minsky, M., & Papert, S. (1969). Perceptrons. MIT Press, Cambridge, MA.
  • Narendra, K. S., & Parthasarathy, K. (1990). Indentification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
  • Nilson, N. J. (1965). Learning Machines. McGraw-Hill.
  • Öztemel, E. (2012). Yapay Sinir Ağları (Vol. 3). İstanbul: Papatya Yayıncılık Eğitim.
  • Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychoanalytic Review, 65, 386-408.
  • Rumelhart, D. E., Hinton, D. E., & Williams, R. J. (1986). Learning representation by backpropagating errors. Nature, 323(9), 533-536.
  • Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing, explorations in the microstructure of cognition. Foundations, MIT Press Cambridge, MA, 1.
  • Rumelhart, D. E., & McClelland, J. L. (1988). Parallel distributed processing, explorations in the microstructure of cognition, A handbook of models, programs and exercies. MIT Press, Cambridge, MA.
  • Specht, D. F. (1988). Probabilistic neural networks for classification, mapping or associative memory. IEEE Conference on Neural Networks, 1, 525-532.
  • Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568-576.
  • Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. WESTCON Convention, Record Part IV, 96-104.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Mustafa Furkan Keskenler

Eyüp Fahri Keskenler

Yayımlanma Tarihi 29 Aralık 2017
Gönderilme Tarihi 24 Ekim 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 5 Sayı: 2

Kaynak Göster

APA Keskenler, M. F., & Keskenler, E. F. (2017). Geçmişten Günümüze Yapay Sinir Ağları ve Tarihçesi. Takvim-I Vekayi, 5(2), 8-18.

Takvim-i Vekayi (تقويم وقايع)