A Novel Associative Memory System Based Modeling and Prediction of TCP Network Traffic

摘要

This paper proposes a novel high-order associative memory system (AMS) based on the Newton's forward interpolation (NFI), The Interpolation Polynomials and training algorithms for the new AMS scheme are derived. The proposed novel AMS is capable of implementing error-free approximation to complex nonlinear functions of arbitrary order. A method Based on NFI-AMS is designed to model and predict network traffic dynamics, which is capable of modeling the complex nonlinear behavior of a traffic time series and capturing the properties of network traffic. The simulation results showed that the proposed scheme is feasible and efficient. Furthermore, the NFI-AMS based traffic prediction can be used in more fields for network design, management and control.