Attractor Control Using Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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 W Abel

Citations