• A novel computing approach to recognizin

    From ScienceDaily@1:317/3 to All on Thu Apr 14 22:30:46 2022
    A novel computing approach to recognizing chaos

    Date:
    April 14, 2022
    Source:
    Springer
    Summary:
    A new paper proposes using the single nonlinear node delay-based
    reservoir computer to identify chaotic dynamics.



    FULL STORY ========================================================================== Chaos isn't always harmful to technology, in fact, it can have several
    useful applications if it can be detected and identified.


    ========================================================================== Chaos and its chaotic dynamics are prevalent throughout nature and through manufactured devices and technology. Though chaos is usually considered
    a negative, something to be removed from systems to ensure their optimal operation, there are circumstances in which chaos can be a benefit and
    can even have important applications. Hence a growing interest in the
    detection and classification of chaos in systems.

    A new paper published in EPJ Bauthored by Dagobert Wenkack Liedji
    and Jimmi Herve' Talla Mbe' of the Research unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, University
    of Dschang, Cameroon, and Godpromesse Kenne', from Laboratoire d'
    Automatique et d'Informatique Applique'e, Department of Electrical
    Engineering, IUT-FV Bandjoun, University of Dschang, Cameroon, proposes
    using the single nonlinear node delay-based reservoir computer to identify chaotic dynamics.

    In the paper, the authors show that the classification capabilities of
    this system are robust with an accuracy of over 99 per cent. Examining
    the effect of the length of the time series on the performance of the
    method they found higher accuracy achieved when the single nonlinear
    node delay-based reservoir computer was used with short time series.

    Several quantifiers have been developed to distinguish chaotic dynamics
    in the past, prominently the largest Lyapunov exponent (LLE), which is
    highly reliable and helps display numerical values that help to decide
    on the dynamical state of the system.

    The team overcame issues with the LLE like expense, need for the
    mathematical modelling of the system, and long-processing times by
    studying several deep learning models finding these models obtained poor classification rates. The exception to this was a large kernel size convolutional neural network (LKCNN) which could classify chaotic and nonchaotic time series with high accuracy.

    Thus, using the Mackey-Glass (MG) delay-based reservoir computer system to classify nonchaotic and chaotic dynamical behaviours, the authors showed
    the ability of the system to act as an efficient and robust quantifier
    for classifying non-chaotic and chaotic signals.

    They listed the advantages of the system they used as not necessarily
    requiring the knowledge of the set of equations, instead, describing the dynamics of a system but only data from the system, and the fact that neuromorphic implementation using an analogue reservoir computer enables
    the real-time detection of dynamical behaviours from a given oscillator.

    The team concludes that future research will be devoted to deep reservoir computers to explore their performances in classifications of more
    complex dynamics.


    ========================================================================== Story Source: Materials provided by Springer. Note: Content may be edited
    for style and length.


    ========================================================================== Journal Reference:
    1. Dagobert Wenkack Liedji, Jimmi Herve' Talla Mbe', Godpromesse
    Kenne'.

    Chaos recognition using a single nonlinear node delay-based
    reservoir computer. The European Physical Journal B, 2022; 95 (1)
    DOI: 10.1140/ epjb/s10051-022-00280-6 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220414125140.htm

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