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

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.