Simulation And Implementation Of Hybrid Network Traffic Prediction Model Based On Optimized Neural Network

Modern social network in the information society has an increasingly important role.Internet technology enables the efficient communication,improves people's quality of life,and also contributes to further development in mathematics,information science and other scientific fields.With more and more base stations constructed,the network environment has become more complex.The research on the model,characteristics and reliability of network traffic is essential..The research results will benefit all kinds of problems such as network engineering,network security and network service.In this paper,the network traffic is analyzed based on the hybrid neural network model.The main contribution is summarized as follows:a)The network traffic data set is studied,and its chaotic property is verified by analyzing the chaos characteristics of the data.b)The time series prediction method is surveyed,including the traditional time series analysis and chaotic time series analysis method.The autoregressive moving average model is studied in detail.The model has some shortcomings in the prediction of network traffic,and it needs the prediction model with higher reliability and higher accuracy.c)Based on the study of artificial neural network,wavelet transform theory and quantum genetic algorithm,a neural network optimization method based on efficient global search capability of quantum genetic algorithm is proposed.This method applies the wavelet transform to data analysis,and preserves the robustness and nonlinear processing ability of the artificial neural network.Based on the optimized neural network,the hybrid network traffic is predicted.The prediction model is named as artificial neural network model based on quantum genetic search.d)Using the proposed model,the single-step and multi-step prediction of network traffic data is carried out and the prediction results are evaluated.Comparing with the results of self-regression and moving average model,the superiority of the new model in self-adaptability and accuracy is verified.In this thesis,the proposed quantum genetic artificial neural network model can predict the network traffic more accurately.The prediction results can be used to monitor the network anomaly in the network security field,and can be used to improve the service quality in the network.The results will also benefit to search efficient network optimization solutions by predicting network behavior.