Research On Chaos Property And Prediction Method Of Telephone Traffic Demand In Mobile Communiction Networks

With the popularity of mobile telephone in China, mobile operator has been paying more and more attention to the prediction of the telephone traffic demand. The accuracy of the prediction result directly concerns the future development of enterprise. Therefore, research on the prediction of the telephone traffic demand is increasingly urgent. In this paper, the following aspects have been mainly studied.1. The various international prediction methods which have offered basis for the research in this paper are summarized.2. The telephone traffic demand is nonlinear system affected by various factors. The value of the optimal delay time lag and embedding dimension are estimated through phase space reconstruction. Largest Lyapunov exponent and predictable time are acquired by applying Wolf algorithm. The research result has shown Chaos property and predictability of the time-series of the telephone traffic demand.3. Since the time-series of the telephone traffic demand show complex nonlinearity and chaos property, and neural network has the strong ability to map nonlinear and can theoretically approach nonlinear functions in any form with high precision, and has the powerful learning ability, therefore, neural network is applied to the prediction of the time-series of the telephone traffic demand. Firstly, Back-Propagation neural network is used. Then, recurrent neural network ?Elman network which has more dynamic property is adopted to predict the telephone traffic demand, because the time-series of the telephone traffic demand not only has chaos property , but also has the properties such as tendency and periodicity. It has raised prediction precision.4. Any single prediction method is confronted with the enormous risk of prediction error owing to the limitation of information used. Combined prediction method, whose prediction value is acquired through putting different weight to theprediction value of some kinds of prediction methods, therefore, has been put forward. Because genetic algorithm has advantages such as the optimization of overall situation, parallel handling property and versatility, strong stability etc, the adaptive parameter real-coded genetic algorithm(APRGA) is introduced to solve the weight coefficients of combined prediction. The result of simulation has shown that the precision of prediction is raised.5. Design idea of prediction system of telephone traffic demand in mobile communication networks has also been systematically put forward.What has been mentioned above is not only theoretical guidance to predict the telephone traffic demand for mobile operator, but also beneficial beginning of the research on prediction of telephone demand traffic in mobile communication networks.