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Application of Neuro-Controller Models for Adaptive Control

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Recent Developments in Data Science and Intelligent Analysis of Information (ICDSIAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 836))

Abstract

In this paper, a method for constructing a model of a controller based on recurrent neural network architecture for implementation of control for the optimal trajectory finding problem is considered. A type of a neuro-controller based on recurrent neural network architecture with long short-term memory blocks as a knowledge base on the external environment and previous states of the controller is proposed. The formalization of the technological cycle of a special type for adaptive control of a production process using the model of the neuro-controller is given.

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Author information

Authors and Affiliations

  1. Francisk Skorina Gomel State University, Gomel, Belarus

    Viktor Smorodin & Vladislav Prokhorenko

Authors
  1. Viktor Smorodin
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  2. Vladislav Prokhorenko
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Corresponding author

Correspondence to Viktor Smorodin .

Editor information

Editors and Affiliations

  1. Applied Mathematics Department, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine

    Oleg Chertov

  2. Department of Economics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Tymofiy Mylovanov

  3. Department of Intelligent Information Systems, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

    Yuriy Kondratenko

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  5. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  6. Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia

    Vadim Stefanuk

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Smorodin, V., Prokhorenko, V. (2019). Application of Neuro-Controller Models for Adaptive Control. In: Chertov, O., Mylovanov, T., Kondratenko, Y., Kacprzyk, J., Kreinovich, V., Stefanuk, V. (eds) Recent Developments in Data Science and Intelligent Analysis of Information. ICDSIAI 2018. Advances in Intelligent Systems and Computing, vol 836. Springer, Cham. https://doi.org/10.1007/978-3-319-97885-7_4

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