DocumentCode :
1972834
Title :
Using the optimized radial basis function neural network and instantanous features for ARCS
Author :
Ebrahimzadeh, Ataollah ; Hossienzadeh, Mahdi
Author_Institution :
Fac. of Electr. & Comput. Eng., Babol Univ. of Technol., Babol, Iran
fYear :
2011
fDate :
16-18 Sept. 2011
Firstpage :
5159
Lastpage :
5162
Abstract :
Automatic recognition of the communication signals (ARCS)plays an important role for various applications, this paper presents a hybrid intelligent system that automatically recognizes a variety of digital communication signals. The hybrid system includes three main modules: feature extraction module, classifier module and optimization module. In the feature extraction module, we have used the balanced combination of the instantaneous features and other features of the signals. In the classifier module, a radial basis function neural network is proposed as the classifier. In the optimization module it is optimized the recognizer design by genetic algorithm for selection the best structure of the classifier. Simulation results show that the proposed technique has very high recognition accuracy even at very low SNRs.
Keywords :
feature extraction; genetic algorithms; radial basis function networks; signal classification; automatic communication signals recognition; classifier module; digital communication signals; feature extraction module; genetic algorithm; hybrid intelligent system; instantanous features; optimization module; optimized radial basis function neural network; Accuracy; Feature extraction; Genetic algorithms; Modulation; Neurons; Signal to noise ratio; Training; communication signals recognition; fourth order moment; genetic algorithm; instantaneous features; radial basis neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Control Engineering (ICECE), 2011 International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4244-8162-0
Type :
conf
DOI :
10.1109/ICECENG.2011.6057022
Filename :
6057022
Link To Document :
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