Prediction of dynamical systems by symbolic regression.
Identifieur interne : 001919 ( Main/Exploration ); précédent : 001918; suivant : 001920Prediction of dynamical systems by symbolic regression.
Auteurs : Markus Quade [Allemagne] ; Markus Abel [Allemagne] ; Kamran Shafi [Australie] ; Robert K. Niven [Australie] ; Bernd R. Noack [Allemagne]Source :
- Physical review. E [ 2470-0053 ] ; 2016.
Abstract
We study the modeling 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 generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized 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.
DOI: 10.1103/PhysRevE.94.012214
PubMed: 27575130
Affiliations:
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E</title><idno type="eISSN">2470-0053</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We study the modeling 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 generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized 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.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>Australie</li></country><region><li>Brandebourg</li></region><settlement><li>Potsdam</li></settlement></list><tree><country name="Allemagne"><region name="Brandebourg"><name sortKey="Quade, Markus" sort="Quade, Markus" uniqKey="Quade M" first="Markus" last="Quade">Markus Quade</name></region><name sortKey="Abel, Markus" sort="Abel, Markus" uniqKey="Abel M" first="Markus" last="Abel">Markus Abel</name><name sortKey="Noack, Bernd R" sort="Noack, Bernd R" uniqKey="Noack B" first="Bernd R" last="Noack">Bernd R. Noack</name></country><country name="Australie"><noRegion><name sortKey="Shafi, Kamran" sort="Shafi, Kamran" uniqKey="Shafi K" first="Kamran" last="Shafi">Kamran Shafi</name></noRegion><name sortKey="Niven, Robert K" sort="Niven, Robert K" uniqKey="Niven R" first="Robert K" last="Niven">Robert K. Niven</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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