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Dudnyk, Volodymyr [Author]; Sinenko, Yuriy [Author]; Matsyk, Mykhailo [Author]; Demchenko, Yevhen [Author]; Zhyvotovskyi, Ruslan [Author]; Repilo, Iurii [Author]; Zabolotnyi, Oleg [Author]; Simonenko, Alexander [Author]; Pozdniakov, Pavlo [Author]; Shyshatskyi, Andrii [Author]

Development of a Method for Training Artificial Neural Networks for Intelligent Decision Support Systems

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  • Media type: E-Book
  • Title: Development of a Method for Training Artificial Neural Networks for Intelligent Decision Support Systems
  • Contributor: Dudnyk, Volodymyr [VerfasserIn]; Sinenko, Yuriy [VerfasserIn]; Matsyk, Mykhailo [VerfasserIn]; Demchenko, Yevhen [VerfasserIn]; Zhyvotovskyi, Ruslan [VerfasserIn]; Repilo, Iurii [VerfasserIn]; Zabolotnyi, Oleg [VerfasserIn]; Simonenko, Alexander [VerfasserIn]; Pozdniakov, Pavlo [VerfasserIn]; Shyshatskyi, Andrii [VerfasserIn]
  • imprint: [S.l.]: SSRN, 2020
  • Extent: 1 Online-Ressource (11 p)
  • Language: English
  • Origination:
  • Footnote: In: Eastern-European Journal of Enterprise Technologies, 3(2 (105)), 37-47 (2020)
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 30, 2020 erstellt
  • Description: A method for training artificial neural networks for intelligent decision support systems has been developed. The method provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function, architecture and parameters of an individual network node. The architecture of artificial neural networks is trained if it is not possible to ensure the specified quality of functioning of artificial neural networks due to the training of parameters of an artificial neural network. The choice of architecture, type and parameters of the membership function takes into account the computing resources of the tool and the type and amount of information received at the input of the artificial neural network. The specified method allows the training of an individual network node and the combination of network nodes. The development of the proposed method is due to the need for training artificial neural networks for intelligent decision support systems, in order to process more information, with unambiguous decisions being made. This training method provides on average 10–18 % higher learning efficiency of artificial neural networks and does not accumulate errors during training. The specified method will allow training artificial neural networks, identifying effective measures to improve the functioning of artificial neural networks, increasing the efficiency of artificial neural networks through training the parameters and architecture of artificial neural networks. The method will allow reducing the use of computing resources of decision support systems, developing measures aimed at improving the efficiency of training artificial neural networks and increasing the efficiency of information processing in artificial neural networks
  • Access State: Open Access