Prediction of dynamical systems by symbolic regression
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- @Article{Quade:2016:PhysRevE,
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author = "Markus Quade and Markus Abel and Kamran Shafi and
Robert K. Niven and Bernd R. Noack",
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title = "Prediction of dynamical systems by symbolic
regression",
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journal = "Physical Review E",
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year = "2016",
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volume = "94",
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issue = "1",
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pages = "012214",
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month = "13 " # jul,
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keywords = "genetic algorithms, genetic programming, physics -
data analysis, statistics and probability, nonlinear
sciences - adaptation and self-organising systems,
physics - computational physics",
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publisher = "American Physical Society",
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bibsource = "OAI-PMH server at export.arxiv.org",
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oai = "oai:arXiv.org:1602.04648",
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URL = "http://arxiv.org/abs/1602.04648",
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URL = "http://link.aps.org/doi/10.1103/PhysRevE.94.012214",
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DOI = "doi:10.1103/PhysRevE.94.012214",
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size = "15 pages",
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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