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Моделирование по временным рядам в приложении к обработке сложных сигналов

Моделирование по временным рядам в приложении к обработке сложных сигналов

Abstract

Сигналы, снимаемые с большинства реальных систем, особенно живых, нерегулярны, часто хаотичны, нестационарны и зашумлены. Так как современные измерительные приборы обычно реализуют цифровую обработку информации, записи сигналов имеют вид дискретной последовательности отсчетов (временных рядов). В данной лекции дается краткий обзор возможностей обработки таких экспериментальных данных с опорой на реконструкцию и использование предсказательной модели рассматриваемой временной зависимости. Описывается методика реконструкции математических моделей по рядам, которая иллюстрируются примерами из практики автора и его коллег.

Keywords

ВРЕМЕННЫЕ РЯДЫ, ОБРАБОТКА СИГНАЛОВ, РЕКОНСТРУКЦИЯ ДИНАМИЧЕСКИХ СИСТЕМ, ЭМПИРИЧЕСКОЕ МОДЕЛИРОВАНИЕ, ДИАГНОСТИКА СВЯЗЕЙ, ПРИЛОЖЕНИЯ В БИОМЕДИЦИНЕ И НАУКАХ О ЗЕМЛЕ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold