The use of artificial neural networks for classification of signal sources in cognitive radio systems

被引：10
|
作者：
Adjemov, S. S. [1 ]
Klenov, N. V. [1 ]
Tereshonok, M. V. [1 ]
Chirov, D. S. [1 ]
机构：
[1] Moscow Tech Univ Commun & Informat, Ul Aviamotornaya 8a, Moscow 111024, Russia
来源：
PROGRAMMING AND COMPUTER SOFTWARE | 2016年 / 42卷 / 03期
关键词：
Artificial Neural Network; Cognitive Radio; Radio Signal; Noise Immunity; Cognitive Radio System;
D O I：
10.1134/S0361768816030026
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In the paper, methods of classification of signal sources in cognitive radio systems that are based on artificial neural networks are discussed. A novel method for improving noise immunity of RBF networks is suggested. It is based on introducing an additional self-organizing layer of neurons, which ensures automatic selection of variances of basis functions and a significant reduction of the network dimension. It is shown that the use of auto-associative networks in the problem of the classification of sources of signals makes it possible to minimize the feature space without significant deterioration of its separation properties.
引用
收藏
页码：121 / 128
页数：8
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