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Artificial Intelligence Systems Based on Artificial Neural Networks in Ecology

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Cybernetics Perspectives in Systems (CSOC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 503))

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Abstract

Due to the discovery of the Eskov-Zinchenko effect throughout biomedicine and biocybernetics, any prospects lack for algorithmic artificial intelligence systems further use becomes clear. The authors prove special opportunities for using artificial neural networks in artificial intelligence systems. By the industrial ecology problem example, the neural networks’ capabilities for finding order parameters (system synthesis) are revealed. Such problems solution within the already existing neural networks framework is impossible, as the authors use two special modes (chaos and multiple reverberations - retuning) of artificial neural networks. #CSOC1120.

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Authors and Affiliations

  1. FGU The FNTS Scientific Research Institute for System Research of the Russian Academy of Sciences, The Separate Division FNTS NIISR RAS in Surgut, 34, Basic St., Surgut, 628426, Russia

    G. V. Gazya

  2. Surgut State University, 1 Lenina Ave., Surgut, 628408, Russia

    V. V. Eskov, T. V. Gavrilenko & N. F. Stratan

Authors
  1. G. V. Gazya
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  2. V. V. Eskov
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  3. T. V. Gavrilenko
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  4. N. F. Stratan
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Corresponding author

Correspondence to G. V. Gazya .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Radek Silhavy

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Gazya, G.V., Eskov, V.V., Gavrilenko, T.V., Stratan, N.F. (2022). Artificial Intelligence Systems Based on Artificial Neural Networks in Ecology. In: Silhavy, R. (eds) Cybernetics Perspectives in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-09073-8_14

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