為了要克服網路交通流量的非線性以及時變的特性，我們將網路的交通流量分成兩個種類。第一種交通流量是來自可以控制的資料來源，我們使用模糊的有輔助輸入的自回歸移動平均（fuzzy ARMA model with auxiliary input）模型來加以描述。另外一種交通流量是來自不可控制的資料來源，我們把他們當成外部的干擾。為了要克服在壅塞控制設計中，常見的傳輸延遲困難，模糊的交通流量模型被轉換成一個等效的模糊交通流量預測模型。根據這個模型，我們推導出一個最佳的網路流量控制方法。這個方法不但能夠避免壅塞的發生，而且可以維持高使用率以及事先預定的服務品質。接著我們使用適應性訊號處理的NLMS演算法，即時地估出之前的模型所使用到的參數，並且利用這些參數來計算流量控制。最後再透過一個限制器，來限制負的（不合理的）及過大的控制流量，以避免網路交換機中緩衝器的長度激烈地變動。藉由模糊的技巧，可以克服網路交通流量中非線性的特性。藉由適應性的技巧，可以克服時變的特性。藉由預測的技巧，可以克服傳輸延遲的困擾。所以，我們提出的模糊適應性預測的非同步傳輸模式網路交通流量控制方法，可以成功地克服以上提到的困難，並且達到不錯的表現。最後列出一些模擬比較的結果，顯示出我們提出的方法的確比其他方式優異。

In order to exploit the nonlinear time-varying property of network traffic,
the traffic flow from controlled sources is described by fuzzy autoregressive

moving-average model with auxiliary input(ARMAX process),

with the traffic flow from uncontrolled sources(i.e., cross traffic)

being described as external disturbances. In order to overcome the

difficulty of the transmission delay in the design of congestion control,

the fuzzy traffic model is translated to an equivalent fuzzy predictive

traffic model. A fuzzy adaptive flow control scheme is proposed to avoid

congestion at high utilization while maintaining the quality of service at a

prescribed level. By use of fuzzy adaptive prediction technique, the

congestion control design difficulties due to nonlinearity, time-varying

characteristics and large propagation delay are overcome by the proposed

adaptive traffic control method. A comparative evaluation is also given to indicate

the superiority of the proposed method.

1, V.Frost and B.Melamed, ''Traffic modeling for telecommunication networks,''IEEE Commun. Mag., pp. 70-81, Mar. 1994.
2. A.Adas, ''Traffic models in broadband networks,''IEEE Commun. Mag., pp. 82-89, July 1997.
3. H.Heffes and D.Lucantoni, ''A markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance,''IEEE JSAC, pp.856-868, Sept. 1986.
4. C.Shim et al., ''Modeling and call admission control algorithm of variable bit rate video in ATM networks,''IEEE JSAC, Vol.12, No.2, pp.332-344, Feb. 1994.
5. W.E.Lehand, M.S. Taqqu, W.Willinger and D.V.Wilson, ''On the self-similar nature of Ethernet traffic(extended version),'' IEEE/ACM Trans., Networking, Vol.2, No.1, pp. 1-15, Feb. 1994.
6. J.Beran, et al.,''Long-range dependence in variable-bit-rate video traffic,''IEEE Trans. Commun. Vol.43, No.7, pp. 1566-1597, July 1995.
7. Z.Fan and P.Mars,''Access flow control scheme for ATM network using neural network-based traffic prediction,'' IEE proc. Commu., Vol.144, No.5, pp.295-300, Oct, 1997.
8. L.Benrnohamed and S.M.Meerkov, ''Feedback control of congestion in packet switch networks: the case of single congested node,''IEEE/ACM Trans. Networking, Vol.1, No.6, pp.693-708, Dec. 1993.
9. I.W.Habib and T.N.Saadawi,''Access flow control algorithms in broadband networks,''Comput. Commun., Vol.15, No.5, pp. 326-332,1992.
10. A.Pitsillides and J.Lambert,''Adaptive congestion control in ATM based networks: quality of service and high utilisation,''Computer Communication, pp. 1239-1258, 1997.
11. T. M. Chen, S. S. Liu, and V. K. Samalam, ''The Available Bit Rate Service for Data in ATM Networks,''IEEE Commun. Mag., pp 56-71, May 1996.
12. B. Maglaris, D. Anastassiou, P.Sen, G. Karlsson, and J. D. Robbins, ''Performance Models of Statistical Multiplexing in Packet Video Communications,'' IEEE Trans. Commun. Vol.36, No.7, pp. 834-844, July 1988
13. M. Schawartz, Broadband Integrated Networks, Prentice Hall, 1996.
14. D. P. Heyman, ''The GBAR Source Model for VBR Videoconferences,'' IEEE/ACM Trans. Networking, Vol. 5, No. 4, pp. 554-560, Aug. 1997.
15. S. Haykin, Adaptive Filter Theory, 2nd Ed. Prentice Hall, 1996.
16. R. Q. Hu and D. W.Petr, ''A predictive Self-Tuning Fuzzy-Logic Feedback Rate Controller,'' IEEE/ACM Trans. Networking, Vol. 8, No. 6, pp. 697-709, Dec. 2000.
17. G.C. Goodwin and K.S. Sin, Adaptive filtering prediction and control, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1984.
18. I. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall Inc., Englewood Cliffs, New Jersey, 1997.
19. T. Takagi and M. Sugeno, ''Fuzzy identification of systems and its applications to modeling and control,'' IEEE Trans. Syst., Man, Cybern., Vol. 15, pp. 116-132, Jan/Feb, 1985.
20. R. G. Cheng and C. J. Chang, ''Design of a fuzzy traffic controller for ATM network,'' IEEE/ACM Trans. Networking, vol. 4, No. 3, pp. 460-469, Jun. 1996.
21. The ATM Forum Traffic Management Specification, V 4.0, ATM Forum, April 1996.
22. B. Qiu, ''The effect of traffic prediction on ABR buffer requirement,'' APCC/OECC '99, vol.1, pp. 151-154, 1999.
23. D.V. Lindley, ''The theory of queues with a single server,'' Proc. Camb. Phil. Soc., Vol. 48, pp. 277-289, 1952.