Attractor Control Using Machine Learning

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@Misc{oai:arXiv.org:1311.5250,
  title =        "Attractor Control Using Machine Learning",
  author =       "Thomas Duriez and Vladimir Parezanovic and 
                 Bernd R. Noack and Laurent Cordier and Marc Segond and 
                 Markus Abel",
  year =         "2013",
  month =        "22 " # nov,
  howpublished = "arXiv",
  keywords =     "genetic algorithms, genetic programming, nonlinear
                 sciences, chaotic dynamics, physics, fluid dynamics,
                 ECJ",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1311.5250",
  URL =          "http://arxiv.org/abs/1311.5250",
  size =         "5 pages",
  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.",
  notes =        "Comment: 5 pages, 4 figures",
}

Genetic Programming entries for Thomas Duriez Vladimir Parezanovic Bernd R Noack Laurent Cordier Marc Segond Markus Abel

Citations