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Представление случайных процессов с помощью неканонического разложения

Представление случайных процессов с помощью неканонического разложения

Abstract

При решении задачи статистической динамики требуется по известным вероятностным характеристикам входной случайной функции строить ее выборочные функции реализации. Данная проблема решается представлением случайных процессов в виде детерминированных функций некоторой совокупности случайных величин. Наиболее распространены линейные канонические разложения случайных функций, которые удобно использовать при анализе линейных систем. Для решения нелинейной задачи статистической динамики каноническое разложение по базисным координатным функциям трудно реализуемо. В настоящей работе применяется нелинейная неканоническая форма представления случайных процессов, предложенная в [1].

When solving a problem of statistic dynamics it is necessary to build its selected functions-realizationsby the known probability characteristics of an input random function. This problem is solved by presenting random processes as determinate functions of a set of random variables. Linear canonical decompositions of random functions are most commonly used. They are convenient to use in linear system analysis. It is, however, hardly feasible to use canonical decomposition on basic coordinate functions in order to solve a non-linear problem. This paper deals with a non-linear non-canonical form of presenting random processes proposed in [1].

Keywords

СТАЦИОНАРНЫЙ СЛУЧАЙНЫЙ ПРОЦЕСС, КОРРЕЛЯЦИОННАЯ ТЕОРИЯ, МЕТОД ИНТЕРПОЛЯЦИОННЫХ ПОЛИНОМОВ, НЕЛИНЕЙНОЕ НЕКАНОНИЧЕСКОЕ РАЗЛОЖЕНИЕ

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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