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