Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems
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- @Article{karaseva:2022:Algorithms,
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author = "Tatiana Karaseva and Eugene Semenkin",
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title = "Evolutionary Approaches to the Identification of
Dynamic Processes in the Form of Differential Equations
and Their Systems",
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journal = "Algorithms",
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year = "2022",
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volume = "15",
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number = "10",
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pages = "Article No. 351",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1999-4893",
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URL = "https://www.mdpi.com/1999-4893/15/10/351",
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DOI = "doi:10.3390/a15100351",
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abstract = "Evolutionary approaches are widely applied in solving
various types of problems. The paper considers the
application of EvolODE and EvolODES approaches to the
identification of dynamic systems. EvolODE helps to
obtain a model in the form of an ordinary differential
equation without restrictions on the type of the
equation. EvolODES searches for a model in the form of
an ordinary differential equation system. The
algorithmic basis of these approaches is a modified
genetic programming algorithm for finding the structure
of ordinary differential equations and differential
evolution to optimise the values of numerical constants
used in the equation. Testing for these approaches on
problems in the form of ordinary differential equations
and their systems was conducted. The influence of noise
present in the data and the sample size on the model
error was considered for each of the approaches. The
symbolic accuracy of the resulting equations was
studied. The proposed approaches make it possible to
obtain models in symbolic form. They will provide
opportunities for further interpretation and
application.",
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notes = "also known as \cite{a15100351}",
- }
Genetic Programming entries for
Tatiana Karaseva
Eugene Semenkin
Citations