A Neural-Network Method for the Synthesis of Informative Features for the Classification of Signal Sources in Cognitive Radio Systems

Adjemov, S.S.; Klenov, N.V.; Tereshonok, M.V.; Chirov, D.S.

Информация о цитировании статьи получена из Web of Science, Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 19 июля 2016 г.

Авторы: Adjemov S.S., Klenov N.V., Tereshonok M.V., Chirov D.S.
Журнал: Moscow University Physics Bulletin
Том: 71
Номер: 2
Год издания: 2016
Издательство: Allerton Press
Местоположение издательства: New York, N.Y., United States
Первая страница: 174
Последняя страница: 179
DOI: 10.3103/S0027134916020028
Аннотация: This paper discusses possible methods for the synthesis of informative features for the classification of signal sources in cognitive radio systems using artificial neural networks. A synthesis method based on the use of autoassociative neural networks is proposed. From the point of view of the classification of the signals, informativeness of synthesized features is estimated using a modified artificial neural network based on radial basis functions that contains an additional self-organizing layer of neurons that provide the automatic selection of the variance of basis functions and a significant reduction of the network dimension. It is shown that the use of autoassociative networks in the problem of the classification of signal sources makes it possible to synthesize the feature space with a minimum dimension while maintaining separation properties.
Добавил в систему: Кленов Николай Викторович

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A Neural-Network Method for the Synthesis of Informative Features for the Classification of Signal Sources in Cognitive Radio Systemsстатья

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