Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

4th International Conference on Mechanics and Mechatronics Research

机械制造;无线电电子学

Yang, Bin^1

School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China^1

关键词: Artificial bee colony algorithms (ABC);    Complex-valued neural networks;    Forecasting accuracy;    Network dynamics;    Network traffic measurement;    Network traffic predictions;    Small time scale;    Traffic measurements;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/224/1/012044/pdf
DOI  :  10.1088/1757-899X/224/1/012044

来源: IOP

【 摘 要 】

Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately.