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中文摘要：
研究了不准确网络信息下的流量优化．提出虚容量的概念，建立了基于本地状态信息的网络流量优化模型；提出的分布式实时无导师学习算法，根据网络流量变化的幅度和频度判断是否需要优化并行路径间的流量分配并且自适应的调整．该算法不需要统计、刷新和存储网络中的各种状态信息以及流量矩阵，仿真证明其优化效果明显．
英文摘要：
The traffic optimization under the inaccurate network information was discussed. Defining the virtual capacity, a model was made on the local state information; a distributed real-time and unsupervised learning algorithm was proposed, which can learn the range and frequency of the network traffic variation and then choose adaptively whether or not and how to adjust the traffic distribution between parallel paths. This algorithm can optimize the network traffic efficiently. It need not to make statistics, refresh and keep all kinds of state information and traffic matrix in the network.
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