Adaptive Method for Estimating Traffic Characteristics in Corporate Multi-Service Communication Networks for Transport Companies

Abstract

An adaptive method for estimation the traffic characteristics in high-speed corporate multiservice networks based on the methods of preliminary indistinct computer training, functioning in real time mode, is proposed and investigated in this paper. The relevance of the study is due to the fact that many processes of network management in high-speed corporate multiservice communication networks need to be implemented in a mode close to real time. The approach proposed in the paper is based on the concept of conditional nonlinear Pareto-optimal filtering V. C. Pugachev. The essence of this approach consists in the fact that estimation of the traffic parameter is performed in two stages - on the first stage the parameter value prediction is estimated, and on the second stage, when the next parameter observations are received, the parameter values are corrected. In the proposed method and algorithm, predictions of traffic parameter values are made in a small sliding window, and adaptation is implemented based on pseudo-gradient procedures whose parameters are adjusted using the Takagi-Sugeno indistinct logic inference method. The proposed method and algorithm belong to the class of adaptive methods and algorithms with prior learning. The average relative error of the estimated traffic parameters estimation does not exceed 8.2%, which is a sufficient value for the implementation of operational network management tasks.