Network Traffic Prediction Model of the New Elman Chaotic Neural Network

Abstract:

Considering such characteristics of the network system as nonlinearity, multivariate, and time variation, proposes a new improved Elman neural network model ------SIMF Elman. Seasonal periodicity learning methods are introduced into the learning process of the model. Chaotic search mechanism is introduced in the training process of the network weights. This new model uses the ergodicity of the Tent mapping to optimize the search of chaos variables, reducing data redundancy, and providing effective solution to the local convergence problem. Experimental tests of backbone network egress traffic of a certain university are conducted. The experimental results show that the new model and new algorithms can improve the network training speed and prediction accuracy of network traffic.

• Periodical: Applied Mechanics and Materials (Volumes 220-223)
• Pages: 2546-2554
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.220-223.2546
• Online since: November 2012
• Authors: Xiao Chao Dang, Zhan Jun Hao, Yan Li
• Keywords: Chaotic Search, Network Traffic, New Elman Neural Network, Prediction Model

References

[1] Jie LIU,Yalou HUANG. Nonlinear network traffic prediction based on BP neural network[J]. Journal of Computer Applications,2007,27(7):1770-1772.

[2] Jinsong WANG. Modeling and Prediction for Network Traffic Based on Elman Neural Network [J]. Computer Engineering,2009,35(9):190-191.

[3] QIN SI-ZHAO,SU HONG-TE,MCAVOY THOMAS J.Comparison of for neural net learning methods for dynamic system identification[J].IEEE Transactions on Neural Networks, 1992, 14: 122-130.

DOI: 10.1109/72.105425

[4] Xiaohu SHI.Improved Elman Networks and applications for controlling ultrasonic motors[J]. Applied Artificial Intelligence.2004,18:603-629．

DOI: 10.1080/08839510490483279

[5] Xiaohu SHI,Chunyan LIANG. An Improved Elman Model and Recurrent Back-Propagation Control Neural Networks[J]. Journal of Software,,2003,14(6):1110-1119.

[6] Yuling QIN,Xianren KONG,Wenbo LUO. Model updating based on chaos quantum-behaved particle swarm opti-mization algorithm [J]. Computer Engineering and Applications, 2010, 46(2): 240-242.

DOI: 10.1109/isscaa.2010.5632259

[7] Dongping QIAN. Particle Swarm Optimization Algorithm Based on Tent Chaotic Sequence[J]. Computer Engineering,2010,36(4):180-183.

[8] Liang SHAN，Hao JIANG，Jun LI. Chaotic optimization algorithm based on Tent map [J]. Control and Decision,2005,20(2):179-182.

[9] Kennedy J, Eberhart R. Particle Swarm Optimization[C]//Proc. of IEEE International Conference on Neural Networks. Perth, Australia:[s.n.],1995:1942-1948.