Developed Algorithm for Supervising Identification of Non Linear Systems using Higher Order Statistics: Modeling Internet Traffic

In this work, we use the formulas of statistic techniques for developing an algorithm based on third order moments and autocorrelation function. This algorithm permits to identify non linear system coefficients for recovering the real information from input-output systems. Simulation examples and comparison with other method in the literature are provided to verify the performance of the developed algorithm. The obtained results demonstrate the efficiency and the accuracy of the developed algorithm for non linear system identification under various values of signal to noise ratio (SNR) and different sample sizes N. To corroborate the theoretical results for a real process, we applied the developed algorithm to search a model able to represent the internet traffic data.

[1]  J. Lacoume,et al.  Statistiques d'ordre supérieur pour le traitement du signal , 1997 .

[2]  Richard A. Davis,et al.  Time Series: Theory and Methods (2nd ed.). , 1992 .

[3]  Jitendra K. Tugnait,et al.  Approaches of FIR system identification with noisy data using higher order statistics , 1990, IEEE Trans. Acoust. Speech Signal Process..

[4]  Masao Masugi,et al.  Using a Volterra system model to analyze nonlinear response in video-packet transmission over IP networks , 2007 .

[5]  Said Safi,et al.  Blind parametric identification of non-Gaussian FIR systems using higher order cumulants , 2004, Int. J. Syst. Sci..

[6]  Hong-Zhou Tan,et al.  TOM-based blind identification of nonlinear Volterra systems , 2006, IEEE Transactions on Instrumentation and Measurement.

[7]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[8]  Chrysostomos L. Nikias,et al.  The complex cepstrum of higher order cumulants and nonminimum phase system identification , 1988, IEEE Trans. Acoust. Speech Signal Process..

[9]  M. Iqbal.  An introduction to solar radiation , 1983 .

[10]  S. Billings,et al.  Determination of the analytical parametric relationship for output spectrum of Volterra systems based on its parametric characteristics , 2009 .

[11]  S. Safi,et al.  Modelling solar half-hour data using fourth order cumulants , 2002 .

[12]  Tommy W. S. Chow,et al.  Blind identification of quadratic nonlinear models using neural networks with higher order cumulants , 2000, IEEE Trans. Ind. Electron..

[13]  Jeffrey K. Hollingsworth,et al.  Instrumentation and Measurement , 1998, 2022 International Symposium on Electronics and Telecommunications (ISETC).

[14]  A. Uhl,et al.  Foreword and Editorial International Journal of Future Generation Communication and Networking , .

[15]  Jerry M. Mendel,et al.  Identification of nonminimum phase systems using higher order statistics , 1989, IEEE Trans. Acoust. Speech Signal Process..

[16]  Gérard Favier,et al.  Laguerre-Volterra Filters Optimization Based on Laguerre Spectra , 2005, 2005 13th European Signal Processing Conference.

[17]  Said Safi,et al.  Identification of quadratic systems using higher order cumulants and neural networks: Application to model the delay of video-packets transmission , 2011, Appl. Soft Comput..