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Information Environment for Neural-Network Adaptive Control System

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Advances in Neural Computation, Machine Learning, and Cognitive Research (NEUROINFORMATICS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 736))

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Abstract

The computer architecture for the neural-network control system with a synchronous peripheral subsystem allows transmitting information about the state of the control object and its environment with maximum accuracy. Synchronization between executive resources eliminates restrictions on the interaction between the components of complex control objects. This problem used to be able to be solved only by breaking the problem into slightly dependent smaller tasks. The proposed model is a coherent information environment for neural-network control system, with simultaneous record mode of the object state parameters. This mode is important for the control systems objects with unidentified degrees of freedom – typical field of applications of neural systems. The proposed model of the synchronous information environment is necessary even the control system is implemented with a fixed algorithm.

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Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology (State University), Dolgoprudny, Russia

    Le Ba Chung

  2. Lebedev Institute of Precision Mechanics and Computer Engineering, Moscow, Russia

    Y. A. Holopov

Authors
  1. Le Ba Chung
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  2. Y. A. Holopov
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Corresponding author

Correspondence to Le Ba Chung .

Editor information

Editors and Affiliations

  1. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Boris Kryzhanovsky

  2. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Witali Dunin-Barkowski

  3. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Vladimir Redko

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Chung, L.B., Holopov, Y.A. (2018). Information Environment for Neural-Network Adaptive Control System. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-66604-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66603-7

  • Online ISBN: 978-3-319-66604-4

  • eBook Packages: EngineeringEngineering (R0)

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