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Home > Archive>Volume 12, Issue 1, 2001 >41-48
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Policy of Fuzzy Neural Network Based Congestion Control in High- Speed Network
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    Abstract:

    In this paper, a kind of traffic prediction a nd congestion control policy based on FNN (fuzzy neural network) is proposed for ATM (asynchronous transfer mode). Congestion control is one of the key problems in high-speed networks, such as ATM. Conventional traffic prediction method fo r congestion control using BPN (back propagation neural network) has suffered fr om long convergence time and dissatisfying precision, and it is not effective. T he fuzzy neural network scheme presented in this paper can solve these limitatio ns satisfactorily for its good capability of processing inaccurate information a nd learning. Finally, the performance of the scheme based on BPN is compared wit h the scheme based on FNN using simulations. The results show that the FNN schem e is effective.

    Reference
    [1] ITU Recommendations, I Series (B-ISDN). Ceneva, Switzerlan d: ITU, 1992.
    [2] Fan Z., Mars P. Access flow control scheme for ATM networks using n eural-network-based traffic prediction. In: Newson, P., Rashvand, H.F., eds. I EE Proceedings: Communications. London: IEE Press, 1997,144(5):295～300.
    [3] Habib I., Tarraf A., Saadawi T. A neural network controller for con gestion control in ATM multiplexers. Computer Networks and ISDN Systems, 1997,29 (3):325～334.
    [4] Rughooputh, H.C. Neural tree call admission controller for ATM netw orks. In: Lindblad, T., Padget, L.M., Kinser, M.J., eds. Proceedings of the SPIE (The International Society for Optical Engineering Proceedings of the 9th Works hop in Virtual Intelligence/Dynamic Neural Networks). Bellingham WA USA: SPIE, 1 999. 455～464.
    [5] Melody, M.W., Chen Min-jia, Chu Nui-ming. Traffic prediction and dynamic bandwidth allocation over ATM: a neural network approach. Computer Comm unications, 1995,18(8):563～571.
    [6] Park, Young-Keun, Lee Gyunho. Application of neural networks in hi gh-speed communication networks. IEEE Communications Magazine, 1995,33(10):68～7 4.
    [7] Habib I. Special feature topic on neuralcomputing in high speed net works. IEEE Communications Magazine, 1995,36(10):30～37.
    [8] Naughton, S., Cunningham, P., Somers, F. Asynchronous transfer mode traffic modeling and dimensioning using artificial neural networks. Engineering Applications of Artificial Intelligence, 1999,12(3):321～342.
    [9] Liang Yan-chun, Wang Zheng, Zhou Chun-guang. Application of fuzzy neural networks to the prediction of time series. Computer Research & Developme nt, 1998,35(7):661～665 (in Chinese).梁燕春,王政,周春光.模糊神经网络在时间序列预测中的应用.计算机 研究与发展,1998,35(7):661～665.
    [10] Wang Wei. The Principal of Artificial Neural Network. Beijing: Beijing U niversity of Aeronautics and Astronautics Press, 1995. 52～74 (in Chinese).王伟.人工神经网络原理.北京：北京航空航天大学出版社,1995.52～74.
    [11] Kartam, N., Tongthong, T. Potential of artificial neural networks for re source scheduling. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1997,11(3):171～185.
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何小燕,吴介一,顾冠群.一种基于FNN的高速网络拥塞控制策略.软件学报,2001,12(1):41-48

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History
  • Received:November 15,1999
  • Revised:January 25,2000
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