Abstract
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.
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Original Russian Text © S.S. Adjemov, N.V. Klenov, M.V. Tereshonok, D.S. Chirov, 2016, published in Programmirovanie, 2016, Vol. 42, No. 3.
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Adjemov, S.S., Klenov, N.V., Tereshonok, M.V. et al. The use of artificial neural networks for classification of signal sources in cognitive radio systems. Program Comput Soft 42, 121–128 (2016). https://doi.org/10.1134/S0361768816030026
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DOI: https://doi.org/10.1134/S0361768816030026