This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times

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This work concerns the problem of the identification of the kernels of non-linear quadratic systems using cumulants of the output data corrupted by a Gaussian noise, when the input is a stationary zero mean non-Gaussian white stochastic process. The proposed approach constitutes an extension of linear systems identification algorithm to non-linear quadratic systems using third-order cumulants. The developed algorithm is tested and compared with a recursive least square and a least mean square methods using different quadratic models for various values of signal to noise ratio and different sample sizes N. The simulation results show the efficiency and the accuracy of the proposed algorithm in non-linear quadratic system identification.