Application of the MLP neural networks for analyzing non Gaussian signal

In this investigation we applied the Multi Layer Perceptron (MLP) neural networks for modeling and predicting a real non Gaussian process. The obtained results show that an agreement between predicted and measured values. The statistical error analysis used to evaluate the performance of the correla...

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Bibliographic Details
Published in2011 International Conference on Multimedia Computing and Systems pp. 1 - 4
Main Authors Chabaa, S., Zeroual, A., Antari, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Artificial Neural Network
Artificial neural networks
Biological neural networks
Data analysis
Data models
Frequency measurement
Internet
MLP
Packet transmission data
Prediction
Predictive models
Size measurement
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Summary:In this investigation we applied the Multi Layer Perceptron (MLP) neural networks for modeling and predicting a real non Gaussian process. The obtained results show that an agreement between predicted and measured values. The statistical error analysis used to evaluate the performance of the correlations, between measured and predicted values provides satisfactory results. The developed model is tested and compared with an other model based on Volterra system. The obtained result demonstrates the efficiency of the developed model.
ISBN:1612847307
9781612847306
DOI:10.1109/ICMCS.2011.5945683