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• W3149794949 abstract "We applied a nonlinear time series approach to the traffic measurements obtained at the input of a medium size local area network. In order to reconstruct the underlying dynamical system, we estimated the correlation length and the embedding dimension of traffic series. The estimated embedding dimension, based on the Grassberger – Procaccia algorithm, is high. In order to extract the regular part from the traffic data and to decrease the system’s dimension, we filtered out high-frequency, “noisy” part, applying the wavelet filtering. Using the principal components analysis (PCA), we estimated the number of feature components in the traffic series. The reliable values of the correlation length and the embedding dimension provided the application of a layered neural network for identification and reconstruction of the dynamical system. We have found that the trained neural network reproduces the statistical features of real measurements and confirms the PCA result on the dimension of traffic series." @default.
• W3149794949 created "2021-04-13" @default.
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• W3149794949 date "2004-01-01" @default.
• W3149794949 modified "2023-09-26" @default.
• W3149794949 title "Internet Traffic Dynamics: Local Area Network Study" @default.
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