1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Automaticdifferentialequationsidentification byself-configuringgenetic programmingalgorithm Karaseva T.S.2020 год

Automatic differential equations identification by self-configuring genetic programming algorithm : доклад, тезисы доклада
доклад, тезисы доклада, статья из сборника материалов конференций

Дополнительные ссылки: eLibrary · Источник статьи
База данных: Каталог библиотеки СФУ
Библиографическое описание: Automatic differential equations identification by self-configuring genetic programming algorithm : доклад, тезисы доклада / T. S. Karaseva. - [S. l. : s. n.], 2020. - Текст : непосредственный // IOP Conference Series: Materials Science and Engineering / II International Scientific Conference "Advanced Technologies in Aerospace, Mechanical and Automation Engineering" (2019 ; 18.11 - 21.11 ; Krasnoyarsk) : Institute of Physics and IOP Publishing Limited, 2020. - 734. - P. 12093. - DOI 10.1088/1757-899X/734/1/012093.
Аннотация: The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information needed is only the dynamic system initial point and the sample of input and output effects. The stability of the proposed approach to the presence of noise in the sample and the small amount of data is investigated.
Год издания: 2020
Авторы: Karaseva T.S.
Издательство: Institute of Physics and IOP Publishing Limited
Источник: IOP Conference Series: Materials Science and Engineering
Выпуск: 734
Мероприятие: II International Scientific Conference "Advanced Technologies in Aerospace, Mechanical and Automation Engineering" (Krasnoyarsk, 2019 ; 18.11 - 21.11)
Номера страниц: 12093
Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

Похожие публикации

Hybrid approach tothe dynamic systemsidentification basedon theself-configuringgenetic programmingalgorithm and… Karaseva T.S.Semenkina O.E.2021 год

Hybrid approach to the dynamic systems identification based on the self-configuring genetic…

  • Karaseva T.S. и др.
  • 2021 год
On the automaticidentification ofdifferentialequations using ahybrid evolutionaryapproach Karaseva T.Semenkin E.2021 год

On the automatic identification of differential equations using a hybrid evolutionary approach

  • Karaseva T. и др.
  • 2021 год
Genetic Programmingfor Automatic Designof ParameterAdaptation inDual-PopulationDifferentialEvolution Stanovov VladimirSemenkin Eugene2023 год

Genetic Programming for Automatic Design of Parameter Adaptation in Dual-Population Differential…

  • Stanovov Vladimir и др.
  • 2023 год
Identification ofDifferentialEquations Systemswith Various InputEffects Karaseva T. S.2022 год

Identification of Differential Equations Systems with Various Input Effects

  • Karaseva T. S.
  • 2022 год
Nonlinear dynamicsystem identificationwith a cooperativepopulation-basedalgorithm featuring arestart metaheuristic Brester CRyzhikov IStanovov V2020 год

Nonlinear dynamic system identification with a cooperative population-based algorithm featuring a…

  • Brester C и др.
  • 2020 год
Self-configuringgenetic programmingalgorithm for solvingsymbolic regressionproblems Karaseva T.S.Mitrofanov S.A.2020 год

Self-configuring genetic programming algorithm for solving symbolic regression problems

  • Karaseva T.S. и др.
  • 2020 год
GENETIC PROGRAMMINGALGORITHM FOR THEDYNAMIC SYSTEMSIDENTIFICATION Karaseva T.S.2020 год

GENETIC PROGRAMMING ALGORITHM FOR THE DYNAMIC SYSTEMS IDENTIFICATION

  • Karaseva T.S.
  • 2020 год
Cooperativeself-configuringnature-inspiredalgorithm for ascheduling problem Semenkina O EPopov E ASemenkin E S2021 год

Cooperative self-configuring nature-inspired algorithm for a scheduling problem

  • Semenkina O E и др.
  • 2021 год

Показать ещё

Development andanalysis of aself-configuringdifferentialevolution algorithm Novikov ZakharVakhnin Aleksei2024 год

Development and analysis of a self-configuring differential evolution algorithm

  • Novikov Zakhar и др.
  • 2024 год
Evolutionaryoptimizationalgorithms fordifferential equationparameters, initialvalue and orderidentification Ryzhikov I.Semenkin E.Panfilov I.2016 год

Evolutionary optimization algorithms for differential equation parameters, initial value and…

  • Ryzhikov I. и др.
  • 2016 год
EvolutionaryApproaches to theIdentification ofDynamic Processes inthe Form ofDifferentialEquations and Their… Karaseva T.Semenkin E.2022 год

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential…

  • Karaseva T. и др.
  • 2022 год
SELF-CONFIGURINGGENETIC PROGRAMMINGALGORITHM TO DESIGNDECISION TREES Mitrofanov S.A.2020 год

SELF-CONFIGURING GENETIC PROGRAMMING ALGORITHM TO DESIGN DECISION TREES

  • Mitrofanov S.A.
  • 2020 год
Genetic programmingbased synthesis ofclustering algorithmfor identifyingbatches of electroniccomponents Sopov E.Panfilov I.2019 год

Genetic programming based synthesis of clustering algorithm for identifying batches of electronic…

  • Sopov E. и др.
  • 2019 год
Regularization ofalgorithms forestimation of errorsof differentialequations approximatesolutions Rogalev A.N.Feodorova N.A.2021 год

Regularization of algorithms for estimation of errors of differential equations approximate…

  • Rogalev A.N. и др.
  • 2021 год
APPLICATION OF THESELF-CONFIGURINGGENETIC PROGRAMMINGALGORITHM FORCLASSIFICATIONPROBLEMS SOLVING Karaseva T.S.Semenkin E.S.Maslova O.V.2018 год

APPLICATION OF THE SELF-CONFIGURING GENETIC PROGRAMMING ALGORITHM FOR CLASSIFICATION PROBLEMS…

  • Karaseva T.S. и др.
  • 2018 год
THE RAND INDEX FORCLUSTERING PROBLEMSOLVING APPLYING ASELF-CONFIGURINGGENETIC PROGRAMMINGALGORITHM Bazin N.R.2025 год

THE RAND INDEX FOR CLUSTERING PROBLEM SOLVING APPLYING A SELF-CONFIGURING GENETIC PROGRAMMING…

  • Bazin N.R.
  • 2025 год