A NOVEL COMBINED NETWORK TRAFFIC PREDICTION MODEL IN COGNITIVE NETWORKS
Dandan Li, Xiaomin Zhu, Xiaopu Shang
2010
Abstract
With the development of the network technology, the concept of Cognitive Network has been proposed and studied, and various kinds of algorithms and models in Cognitive Networks thus have become an hot topic of research. This paper proposes a novel model, which includes three stages. The proposed model may achieve a high-precision traffic prediction in cognitive networks. The model solves some problems in cognitive networks, such as low adaptive capability and an easy trap in local optimum when coming up with a fluctuated network flow.
References
- Thomas R W, DaSilva L. A, MacKenzie A B. 2005. New frontiers in dynamic spectrum access networks. 2005 First IEEE International Symposium: 352 - 360
- Kasabov N. 2002. DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. Fuzzy System, IEEE Transactions: 144 - 154
- Feng Huifang, Shu Yantai. 2005. Study on network traffic prediction techniques. Wireless Communications, Networking and Mobile Computing: 1041-1044
- Lv Jun, Li Xing, Congsen Ran, He Tao. 2004. Network traffic prediction and fault detection based on adaptive linear model. IEEE ICIT International Conference. Volume 2: 880 - 885
- Khotanzad A, Sadek N. 2003. Multi-scale high-speed network traffic prediction using combination of neural networks. Neural Networks, Proceedings of the International Joint Conference. Volume 2: 1071-1075
- Wang Peng, Liu Yuan. 2008. Network traffic prediction based on improved BP wavelet neural network. Wireless Communications, Networking and Mobile Computing: 1-5
- Feng Hailiang, Chen Di, Lin Qingjia, Chen Chunxiao. 2006. Multi-scale network traffic prediction using a two-stage neural network. WiCOM International Conference: 1 - 5
- Lei Ting, Yu Zhengwei. 2006. A wavelet neural network model of network traffic forecast. Journal of Computer Application, Volume (3): 526-528 (in Chinese)
- Cheng Guang, Gong Jian, Ding Wei. 2004. Nonlinear-periodical network traffic behavioral forecast based on seasonal neural network model. 2004 Communications, Circuits and Systems: 683-687
- Han Zhijie, Wang Ruchuan. 2008. Novel peer to peer network traffic prediction algorithm. Computer Science, Volume(9): 40-41 (in Chinese)
- Feng Hailiang, Chen Di, Lin Qingjia, Chen Chunxiao. 2006. Combined prediction model of Internet traffic based on neural network. Journal of Computer Application, Volume (9): 108-111 (in Chinese)
- Burg J P. 1975. Maximum entropy spectral analysis. US: Stanford University.
- Ardagna C A, Bernardoni E, Damiani E, Reale S. 2008. Mobile network traffic data compression by means of wavelet decomposition. Second IEEE International Conference on Digital Ecosystems and Technologies : 274-280
- Liu Linhui, Chen Jie, Xu Lixin. 2008. Realization and application research of BP neural network based on MATLAB. Future BioMedical Information Engineering International Seminar: 130 - 133
Paper Citation
in Harvard Style
Li D., Zhu X. and Shang X. (2010). A NOVEL COMBINED NETWORK TRAFFIC PREDICTION MODEL IN COGNITIVE NETWORKS . In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations - Volume 1: ICISO, ISBN 978-989-8425-26-3, pages 205-211. DOI: 10.5220/0003268502050211
in Bibtex Style
@conference{iciso10,
author={Dandan Li and Xiaomin Zhu and Xiaopu Shang},
title={A NOVEL COMBINED NETWORK TRAFFIC PREDICTION MODEL IN COGNITIVE NETWORKS},
booktitle={Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations - Volume 1: ICISO,},
year={2010},
pages={205-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003268502050211},
isbn={978-989-8425-26-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations - Volume 1: ICISO,
TI - A NOVEL COMBINED NETWORK TRAFFIC PREDICTION MODEL IN COGNITIVE NETWORKS
SN - 978-989-8425-26-3
AU - Li D.
AU - Zhu X.
AU - Shang X.
PY - 2010
SP - 205
EP - 211
DO - 10.5220/0003268502050211