caa a22 4500 445297387 CHVBK 20180317142546.0 cr unu---uuuuu 170323e20101001xx s 000 0 eng 10.1007/s12243-010-0180-4 doi (NATIONALLICENCE)springer-10.1007/s12243-010-0180-4 Blind signal-type classification using a novel robust feature subset selection method and neural network classifier [Elektronische Daten] [Ataollah Shermeh, Hamed Azimi] Automatic modulation recognition plays an important role for many novel computer and communication technologies. Most of the proposed systems can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal-to-noise ratio. In this paper, we present a novel hybrid intelligent system that automatically recognizes a variety of digital signals. In this recognizer, a multilayer perceptron neural network with resilient back propagation learning algorithm is proposed as the classifier. For the first time, a combination set of spectral features and higher order moments up to eighth and higher order cumulants up to eighth are proposed as the effective features. Then we have optimized the classifier design by bees algorithm (BA) for selection of the best features that are fed to the classifier. This optimization method is new for this area. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA. Institut Télécom and Springer-Verlag, 2010 Communication format classification nationallicence Bees algorithm nationallicence Resilent back propagation training algorithm nationallicence Multilayer perceptron nationallicence Spectral features nationallicence Higher order moments nationallicence Higher order cumulants nationallicence Shermeh Ataollah Faculty of Electrical and Computer Engineering, Babol University of Technology, P. B. Box4714871167, Babol,, Iran aut Azimi Hamed Faculty of Electrical and Computer Engineering, Babol University of Technology, P. B. Box4714871167, Babol,, Iran aut annals of telecommunications - annales des télécommunications Springer-Verlag 65/9-10(2010-10-01), 625-633 0003-4347 65:9-10<625 2010 65 12243 https://doi.org/10.1007/s12243-010-0180-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12243-010-0180-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shermeh Ataollah Faculty of Electrical and Computer Engineering, Babol University of Technology, P. B. Box4714871167, Babol,, Iran aut NATIONALLICENCE 700 1- Azimi Hamed Faculty of Electrical and Computer Engineering, Babol University of Technology, P. B. Box4714871167, Babol,, Iran aut NATIONALLICENCE 773 0- annals of telecommunications - annales des télécommunications Springer-Verlag 65/9-10(2010-10-01), 625-633 0003-4347 65:9-10<625 2010 65 12243 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer