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dc.contributor.authorGolovko, Vladimir
dc.contributor.authorSavitsky, Yury
dc.contributor.authorManiakov, Nikolaj
dc.contributor.authorRubanov, Vladimir
dc.coverage.spatialMinskru_RU
dc.date.accessioned2023-11-16T13:16:38Z
dc.date.available2023-11-16T13:16:38Z
dc.date.issued2001
dc.identifier.citationSome Aspects of Chaotic Time Series Analysis / Vladimir Golovko [et al.] // The 2nd International Conference on Neural Networks and Artificial Intelligence= Нейронные сети и искусственный интеллект : proceedings, Minsk, October 2–5, 2001 / Belarusian State University of Informatics and Radioelectronicsb ; ed.: Rauf Kh. Sadykhov [et al.]. – Minsk, 2001. – P. 66–69 : il. – Bibliogr.: p. 69 (5 titles).ru_RU
dc.identifier.urihttps://rep.bstu.by/handle/data/37116
dc.descriptionГоловко Владимир Адамович, Савицкий Юрий Викторович, Маняков Николай Владимирович, Рубанов Владимир Степанович. Некоторые аспекты анализа хаотических временных рядовru_RU
dc.description.abstractWe address two aspects in chaotic time series analysis, namely the definition of embedding parameters and the largest Lyapunov exponent. It is necessary for performing state space reconstruction and identification of chaotic behavior. For the first aspect, we examine the mutual information for determination of time delay and false nearest neighbors method for choosing appropriate embedding dimension. For the second aspect we suggest neural network approach, which is characterized by simplicity and accuracy.ru_RU
dc.language.isoenru_RU
dc.publisherBSUIRru_RU
dc.subjectтеория хаосаru_RU
dc.subjectchaos theoryru_RU
dc.subjectembedding parametersru_RU
dc.subjectпараметры встраиванияru_RU
dc.subjectLyapunov exponentru_RU
dc.subjectпоказатель Ляпуноваru_RU
dc.subjectneural networkru_RU
dc.subjectнейронная сетьru_RU
dc.titleSome Aspects of Chaotic Time Series Analysisru_RU
dc.title.alternativeНекоторые аспекты анализа хаотических временных рядовru_RU
dc.typeСтатья (Article)ru_RU


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