Short-term Prediction of Traffic Rate Interval Router Using Dynamic Synapse Neural Network

Majlesi Journal of Electrical Engineering

مقاله 1، دوره 3، شماره 2، خرداد 2009، صفحه 1-5   

نوع مقاله: Review Article

شناسه دیجیتال (DOI): 10.1234/mjee.v3i2.286

نویسندگان

Maryam Shakiba^* ؛ Mohamad Teshnelab؛ Sadan Zokaei

چکیده

Prediction is an important issue in many dynamical systems and is vital for effective management and control of plants. An important process which has recently derived much attention is the congestion control problem. Prediction of different traffic parameters can help in managing a congestion in a computer network. In this thesis, using real data for from the router between Iran Telecommunication Research Center and data Data company during December, January, February and March 2007, the router interval traffic rates are analyzed. Also, a comparative study is performed using the different methods employed and prediction results are provided to show the effectiveness of the predictions. 

کلیدواژه‌ها

Prediction of Time Series؛ en؛ Time Delay Line Neural Network؛ Dynamic Synapse Neural Network and PSO Algorithm

مراجع

[1] Tanenbaum A. S.; “Computer Networks”,

3thd ed., Prentice Hall, 813 pages, ISBN-10:

0133499456, ISBN-13: 978-0133499452.

[2] Box, G.E.P and Jenkins, G. M.; Time Series

Analysis Forecasting and Control, San

Francisco, Holden-Day, 1994.

[3] Thomas Natschl¨ager, Wolfgang Maass,

Anthony Zador; Efficient Temporal

Processing with Biologically Realistic

Dynamic Synapses, Network: Computer.

Neural Syst, pp.75–87, Vol. 12, 2001.

[4] N. Mehrtash, D. Jung, H. Klar; Image

Preprocessing with Dynamic Synapses,

Neural Computer & Application, pp.33-41,

Vol. 12, No. 1 / September, 2003.

[5] J. Gao, Ph. Holmes, On the dynamics of

electrically-coupled neurons with inhibitory

synapses, J Comput Neurosci, pp. 5-100,

Volume 22, Number 1 / February, 2007.

[6] Narayanan Kasthuri, Jeff W Lichtman;

Structural Dynamics of Synapses in Living

Animals, Current Opinion in Neurobiology

2004, 14: pp.105-111, PMID: 15018945.

[7] Mahdi Mahfouf, Min-You Chen, and Derek

Arthur Linkens, Adaptive Weighted Particle

Swarm Optimization for Multi-objective

Optimal Design of Alloy Steels, Department of

Automatic Control and Systems Engineering,

The University of Sheffield, Technical Report,

2004, ISBN: 978-3-540-23092-2, pp. 762-771,

Vol. 3242, 2004.