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2010 | 10D |
Tytuł artykułu

The adaptive system on the basis of artificial neuron networks

Autorzy
Warianty tytułu
RU
Adaptivnaja sistema na osnove iskusstvennykh nejronnykh setej
Języki publikacji
EN
Abstrakty
EN
RU
Wydawca
-
Rocznik
Tom
10D
Opis fizyczny
p.15-24,fig.,ref.
Twórcy
autor
  • Volodymyr Dahl East-Ukrainian University, Lugansk, Ukraine
autor
Bibliografia
  • 1. Zhdanov A.A., 2008.: Autonomous artificial intellect. Mocsow: Binom. Laboratory of base knowledges.
  • 2. Borisov V.V., Kruglov V.V., Fedulov A.S., 2007.: Indistinct models and networks. Moscow: Hot line-Telecom.
  • 3. Egupov N.D., 2004.: Methods of robust, neuro-indistinct and adaptive control. Moscow, Edition of MSTU named after Bauman.
  • 4. Galushkin A.I., 2000.: Neurocomputers. Moscow: IPRRGR.
  • 5. Galushkin A.I., 2000.: Theory of neuron networks. Moscow: IPRRGR.
  • 6.Galushkin A.I., Logovskoy A.S., 1999.: Neurocontrol: main principles and directions of application of neurocomputers for the decision of the tasks of control of dynamic objects. Neurocomputers: development and application. N°l. P.56-66.
  • 7.Galushkin A.I., 2004.: Neurocomputers and their application on the edge of the millennia in China. Moscow: Hot line-Telecom. Vol 1.
  • 8.Gorban A.N., 1990.: Training of neuron networks. Moscow:JV «ParaGraph».
  • 9.Pupkov K.A., Egupov N.D., 2004.: Methods of the classical and modem theory of automation control. Moscow, Edition of MSTU named after Bauman.
  • 10.Terekhov V.A., Efimov D.V., Tyukin I.Y., 1999.: Neuronetwork control systems. SPb: Edition of Saint-Petersburg University.
  • 11.Haykin S., 2005.: Neural Networks - A Comprehensive Foundation, Second Edition, Pearson Education, Inc.
  • 12.Malinetsky G.G., 2005.: Mathematical basis of synergetics. Chaos, structures, calculation experiment. Moscow: ComBook.
  • 13.Ulshin V.A., Yurkov D.A., 2009.: Algorithm of synthesis of neuronetwork structure for the decision of the tasks of classification. Praci of Lugansk Branch of International Informatization. Part 2, N° 2 (20).P. 71-76.
  • 14.Ulshin V.A., Yurkov D.A., 2009.: Optimization of parameters of neuronetwork models with use of evolution methods of search. Visnik DSMA Ns 2 (5E). P. 173-179.
  • 15.Widrow B., Lehr M.A., 1990.: 30 years of adaptive neurol networks: percrptron, madaline and backpropagation . Proceedings of the IEEE. Vol. 78. No 9. P. 1415-1442.
  • 16.Yurkov D.A., 2009.: Method synthesis of neuron network model structures. Visnik of the East Ukrainian National University. Lugansk, Edition of the EUNU named after V. Dal No 5 (135). P. 115-122.
  • 17.Yurkov D.A., 2009.: Synthesis of neuron networks with fractal structure. Easten-European Journal of Enterprise Technologies. No 4-3 (40). Kharkov: Technological center, P. 39-44.
  • 18.Yurkov D.A., 2009.: Classification on the basis of cooperative neuronetwork structures. East Ukrainianjoumal of progressive technologies. No 5-3 (41). Kharkov: Technological center, P 51-56.
  • 19.Yurkov D.A., 2009.: Rising of the effectiveness of decision of the tasks of classification on the basis of neuron networks. East Ukrainian Journal of progressive technologies. No 5-3 (41). Kharkov: Technological center, P. 33-37.
  • 20.Yurkov D.A., 2009.: Method of synthesis of neuron networks for the decision of the tasks of classification. Information technologies and information security in science, engineering and education «INFOTECH-2009». Materials of international scientific-practical conference. Sevastopol: Edition of SevNTU. P. 241-244.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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