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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