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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