Design of computational models for hydroturbine units based on a nonparametric regression approach with adaptation by evolutionary algorithms

Bukhtoyarov Vladimir Viktorovich; Tynchenko Vadim Sergeevich

Дополнительные ссылки: Scopus. All Open Access, Gold · WOS · eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: Design of computational models for hydroturbine units based on a nonparametric regression approach with adaptation by evolutionary algorithms / V. V. Bukhtoyarov, V. S. Tynchenko. - Текст : непосредственный // Computation. - 2021. - Vol. 9, Is. 8. - Ст. 83. - P. 83. - Cited Reference Count: 33. - The reported study was partially funded by the Scholarship of the President of the Russian Federation for young scientists and graduate students SP.869.2019.5., Scholarship of the President of the Russian Federation for young scientists and graduate students [SP.869.2019.5]. - ISSN ISSN- 207, DOI 10.3390/computation9080083.

Аннотация: This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment—hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya–Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of sam-ples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modeling error by 20% and 28% for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonpar-ametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modeling error of about 5% compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Год издания: 2021

Авторы: Bukhtoyarov Vladimir Viktorovich , Tynchenko Vadim Sergeevich

Издательство: MDPI AG

Источник: Computation

Выпуск: Vol. 9, Is. 8

Номера страниц: 83

Держатель оригинала документа: Department of Technological Machines and Equipment of Oil and Gas Complex, School of Petroleum and Natural Gas Engineering, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation, Information-Control Systems Department, Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, 660037, Russian Federation

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Design of computational models for hydroturbine units based on a nonparametric regression approach with adaptation by evolutionary algorithms
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