Prediction of dynamical systems by symbolic regression

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@Article{Quade:2016:PhysRevE,
  author =       "Markus Quade and Markus Abel and Kamran Shafi and 
                 Robert K. Niven and Bernd R. Noack",
  title =        "Prediction of dynamical systems by symbolic
                 regression",
  journal =      "Physical Review E",
  year =         "2016",
  volume =       "94",
  issue =        "1",
  pages =        "012214",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, physics -
                 data analysis, statistics and probability, nonlinear
                 sciences - adaptation and self-organising systems,
                 physics - computational physics",
  publisher =    "American Physical Society",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1602.04648",
  URL =          "http://arxiv.org/abs/1602.04648",
  URL =          "http://link.aps.org/doi/10.1103/PhysRevE.94.012214",
  DOI =          "doi:10.1103/PhysRevE.94.012214",
  size =         "15 pages",
  abstract =     "We study the modelling and prediction of dynamical
                 systems based on conventional models derived from
                 measurements. Such algorithms are highly desirable in
                 situations where the underlying dynamics are hard to
                 model from physical principles or simplified models
                 need to be found. We focus on symbolic regression
                 methods as a part of machine learning. These algorithms
                 are capable of learning an analytically tractable model
                 from data, a highly valuable property. Symbolic
                 regression methods can be considered as generalised
                 regression methods. We investigate two particular
                 algorithms, the so-called fast function extraction
                 which is a generalised linear regression algorithm, and
                 genetic programming which is a very general method.
                 Both are able to combine functions in a certain way
                 such that a good model for the prediction of the
                 temporal evolution of a dynamical system can be
                 identified. We illustrate the algorithms by finding a
                 prediction for the evolution of a harmonic oscillator
                 based on measurements, by detecting an arriving front
                 in an excitable system, and as a real-world
                 application, the prediction of solar power production
                 based on energy production observations at a given site
                 together with the weather forecast.",
}

Genetic Programming entries for Markus Quade Markus W Abel Kamran Shafi Robert K Niven Bernd R Noack

Citations