Genetic Programming based Identification of an Industrial Process
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- @InProceedings{Tarasevich:2021:TSP,
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author = "Maksimilian Tarasevich and Aleksei Tepljakov and
Eduard Petlenkov and Vitali Vansovits",
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title = "Genetic Programming based Identification of an
Industrial Process",
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booktitle = "2021 44th International Conference on
Telecommunications and Signal Processing (TSP)",
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year = "2021",
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pages = "134--140",
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abstract = "In the field of industrial automation, it is essential
to develop and improve mathematical methods that assist
in obtaining more accurate models of real-world
systems. In the following paper, a machine learning
tool is applied to the problem of identifying a model
of an industrial process. Symbolic regression and
genetic programming are a successful combination of
methods using which one can identify a nonlinear model
in analytical form based on data collected from a
process during routine operation. In this paper, a
detailed description of the method implementation as
well as necessary data preprocessing steps are
presented. Then, the resulting models are validated on
an industrial data set and compared on the basis of
performance metrics with more classical methods and
previous results achieved by the authors. Finally, the
encountered problems in the realization of the methods
are reflected upon.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/TSP52935.2021.9522588",
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month = jul,
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notes = "Also known as \cite{9522588}",
- }
Genetic Programming entries for
Maksimilian Tarasevich
Aleksei Tepljakov
Eduard Petlenkov
Vitali Vansovits
Citations