A Comparative Performance Analyses of Training Algorithms Employed in Artificial Neural Networks Based Modulation Recognition Systems

Authors : Büşra ÜLGERLİ

Pages : 178-197

View : 19 | Download : 14

Publication Date : 2016-11-07

Article Type : Research Paper

Abstract :Abstract – The performances of learning algorithms employed in artificial neural networks insert ignore into journalissuearticles values(ANNs); have been analyzed for classifying baseband signals that are subjected to additive white Gaussian noise insert ignore into journalissuearticles values(AWGN); and frequency selective Rayleigh fading channel in this paper. The high order cumulants of the received signals have been utilized in the ANN classifier. Different learning algorithms have been used in finding the optimal weight set which directly affects the performance of artificial neural networks. The performances of Levenberg Marquardt insert ignore into journalissuearticles values(LM); and scaled conjugate gradient insert ignore into journalissuearticles values(SCG); algorithm, the most widely employed learning algorithms, have been compared for training of artificial neural networks. Computer simulation results have demonstrated that the LM-ANN classifier can reach much better classification accuracy than the SCG-ANN recognizer in even low training steps.
Keywords : Modulation recognition, SCG ANN, LM ANN, High order cumul