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利用反向传播算法合理分配缓冲区

吴援明 高科 李乐民

吴援明, 高科, 李乐民. 利用反向传播算法合理分配缓冲区[J]. 电子与信息学报, 2006, 28(8): 1418-1421.
引用本文: 吴援明, 高科, 李乐民. 利用反向传播算法合理分配缓冲区[J]. 电子与信息学报, 2006, 28(8): 1418-1421.
Wu Yuan-ming, Gao Ke, Li Le-min. Managing Buffer Appropriately with the Back Propagation Learning Algorithm[J]. Journal of Electronics & Information Technology, 2006, 28(8): 1418-1421.
Citation: Wu Yuan-ming, Gao Ke, Li Le-min. Managing Buffer Appropriately with the Back Propagation Learning Algorithm[J]. Journal of Electronics & Information Technology, 2006, 28(8): 1418-1421.

利用反向传播算法合理分配缓冲区

Managing Buffer Appropriately with the Back Propagation Learning Algorithm

  • 摘要: 该文提出了一种新的缓冲区分配方法,即动态神经共享 (Dynamic Neural Sharing,DNS)方法。这种方法利用反向传播算法合理分配缓冲区资源,从而减少自相似业务的分组丢失率。通过两组仿真实验发现,与完全分割(Complete Partitioning, CP), 完全共享(Complete Sharing, CS), 部分共享(Partial Sharing, PS)这些传统的缓冲区分配方法相比,DNS在减少分组丢失和体现公平性(每个源都占有一定数量的缓冲区资源)之间达到了更好的平衡。
  • Yousefi'zadeh H, Jonckheere E A, Silvester J A. Utilizing neural networks to reduce packet loss in self-similar teletraffic patterns[J].IEEE International Conference on Communications, Anchorage, AK, USA. ICC0.2003, (3):1942-1946[2]Yuang M C, Tien P L, Chen C S. A contention access protocol with dynamic bandwidth allocation for wireless ATM networks. IEEE International Conference on Communications. New Orleans, USA, ICC2000, 2000, (1): 149-153.[3]Bolla R, Davoli F, Maryni P,et al.. A neural strategy for optimal multiplexing of circuit- and packet-switched traffic[J].Global Telecommunications Conference, Orlando, Florida, USA, GLOBECOM9.1992, (3):1324-1330[4]Long T W. A self-similar neural network for distributed vibration control. Proceedings of the 32nd IEEE Conference on Decision and Control, San Antonio, Texas, USA, 1993, (4): 3243-3248.[5]Mohamed S, Rubino G. A study of real-time packet video quality using random neural networks[J].IEEE Trans. on Circuits and Systems for Video Technology.2002, 12, (12):1071-1083
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出版历程
  • 收稿日期:  2004-12-06
  • 修回日期:  2005-06-27
  • 刊出日期:  2006-08-19

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