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Фильтрация помех и анализ характеристик фильтров на основе адаптивных алгоритмов и нейронной сети adaline

Фильтрация помех и анализ характеристик фильтров на основе адаптивных алгоритмов и нейронной сети adaline

Abstract

В статье рассмотрены существующие методы фильтрации помех с помощью фильтров, использующих адаптивные алгоритмы (метод наименьших квадратов – LMS, метод рекурсивных наименьших квадратов – RLS) и нейронной сети ADALINE. Приведенные временные диаграммы результатов фильтрации сигнала в зависимости от параметров адаптивных алгоритмов фильтрации помех. Проанализированы полученные результаты фильтрации.

The article considers the existing methods of noise filtering by filters using adaptive algorithms (least squares method – LMS and recursive least squares method – RLS), and neural network ADALINE. Time-diagrams of noise filtering results depending on the parameters of adaptive filter algorithms are given. The results of filtration are analyzed and compared.

Keywords

ФіЛЬТРАЦіЯ ЗАВАД, АДАПТИВНі АЛГОРИТМИ, НЕЙРОННА МЕРЕЖА ADALINE, ЧАСОВі ДіАГРАМИ, ФИЛЬТРАЦИЯ ПОМЕХ, АДАПТИВНЫЕ АЛГОРИТМЫ, НЕЙРОННАЯ СЕТЬ ADALINE, ВРЕМЕННЫЕ ДИАГРАММЫ

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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