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

Büşra ÜLGERLİ

Article Details

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

Authors:

Büşra ÜLGERLİ

Publisher:

Tokat Gaziosmanpasa University

Journal:

Journal of New Results in Science 2016 Vol.5, pp.178-197

Document Type:

Article

Publication Date:

11/2016

Document Language:

Turkish

Document ID (Org No.):

2000489972

Frequency:

Tri-annual

pISSN:

1304-7981

Abstract:

 

Abstract – The performances of learning algorithms employed in artificial neural networks (ANNs) have been analyzed for classifying baseband signals that are subjected to additive white Gaussian noise (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 (LM) and scaled conjugate gradient (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.

 


Abstract – The performances of learning algorithms employed in artificial neural networks (ANNs) have been analyzed for classifying baseband signals that are subjected to additive white Gaussian noise (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 (LM) and scaled conjugate gradient (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.

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Article Details

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

Authors: Büşra ÜLGERLİ

Publisher: Tokat Gaziosmanpasa University

Journal: Journal of New Results in Science 2016 Vol.5, pp.178-197

Document Type: Article

Publication Date: 11/2016

Document Language: Turkish

Document ID (Org No.): 2000489972

Frequency: Tri-annual

pISSN: 1304-7981

Issues

Authors:

Büşra ÜLGERLİ

Publisher:

Tokat Gaziosmanpasa University

Journal:

Journal of New Results in Science 2016 Vol.5, pp.178-197

Document Type:

Article

Publication Date:

11/2016

Document Language:

Turkish

Document ID (Org No.):

2000489972

Frequency:

Tri-annual

pISSN:

1304-7981