Энтропийные функционалы и информация различия временных рядов спутникового мониторинга

Ю. Антонов; Антонов Ю. А.; В. Захаров; Захаров В. И.; Н. Сухарева; Сухарева Н. А.

doi:10.31857/S0023420623600149

Энтропийные функционалы и информация различия временных рядов спутникового мониторинга

Authors: Антонов Ю.¹, Захаров В.², Сухарева Н.²^,3
Affiliations:
1. Московский государственный университет им. М.В. Ломоносова, Центр информационных средств и технологий
2. Московский государственный университет им. М.В. Ломоносова
3. Научно-исследовательский институт ядерной физики им. Д.В. Скобельцына, Московский государственный университет им. М.В. Ломоносова
Issue: Vol 61, No 6 (2023)
Pages: 498-509
Section: Articles
URL: https://kld-journal.fedlab.ru/0023-4206/article/view/672572
DOI: https://doi.org/10.31857/S0023420623600149
EDN: https://elibrary.ru/CBUWSZ
ID: 672572

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Abstract
About the authors
References
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Abstract

Обсуждаются результаты информационного анализа временных рядов спутникового мониторинга состояния межпланетного магнитного поля, предоставляемые базой данных Центра космических полетов Годдарда (англ. NASA Goddard Space Flight Center). В арсенале методов анализа основное внимание отводится контролю неэкстенсивных свойств при масштабировании временных выборок, создаваемых на основе временных рядов 2001–2022 гг. Демонстрируется перестройка модальности функции распределения вероятности для компонент межпланетного магнитного поля в рассматриваемом ансамбле реализаций. Представлены результаты реконструкции кинетики энтропийных мер Шеннона–Больцмана–Гиббса, Тсаллиса и Реньи. В неэкстенсивном приближении анализируется влияние q-деформации фазового пространства на стохастические режимы исследуемой системы. В работе впервые совместно исследуются кинетика информационного расхождения и кинетика энтропийных мер ансамбля реализаций значений межпланетного магнитного поля. Указывается необходимость согласования q-параметров деформации фазового пространства, опорной и контролируемой подсистем, в том числе, при использовании асимптотических приближений в методиках прогноза с применением нейросетевых алгоритмов и алгоритмов глубокого обучения.

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