Attractor Control Using Machine Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Misc{oai:arXiv.org:1311.5250,
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title = "Attractor Control Using Machine Learning",
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author = "Thomas Duriez and Vladimir Parezanovic and
Bernd R. Noack and Laurent Cordier and Marc Segond and
Markus Abel",
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year = "2013",
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month = "22 " # nov,
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howpublished = "arXiv",
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keywords = "genetic algorithms, genetic programming, nonlinear
sciences, chaotic dynamics, physics, fluid dynamics,
ECJ",
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bibsource = "OAI-PMH server at export.arxiv.org",
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oai = "oai:arXiv.org:1311.5250",
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URL = "http://arxiv.org/abs/1311.5250",
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size = "5 pages",
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abstract = "We propose a general strategy for feedback control
design of complex dynamical systems exploiting the
nonlinear mechanisms in a systematic unsupervised
manner. These dynamical systems can have a state space
of arbitrary dimension with finite number of actuators
(multiple inputs) and sensors (multiple outputs). The
control law maps outputs into inputs and is optimised
with respect to a cost function, containing physics via
the dynamical or statistical properties of the
attractor to be controlled. Thus, we are capable of
exploiting nonlinear mechanisms, e.g. chaos or
frequency cross-talk, serving the control objective.
This optimisation is based on genetic programming, a
branch of machine learning. This machine learning
control is successfully applied to the stabilisation of
nonlinearly coupled oscillators and maximization of
Lyapunov exponent of a forced Lorenz system. We foresee
potential applications to most nonlinear multiple
inputs/multiple outputs control problems, particularly
in experiments.",
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notes = "Comment: 5 pages, 4 figures",
- }
Genetic Programming entries for
Thomas Duriez
Vladimir Parezanovic
Bernd R Noack
Laurent Cordier
Marc Segond
Markus W Abel
Citations