预测网络业务的行为在通信网络的接入管理和拥塞控制等方面有着重要的意义。
Predicting the behavior of network traffic is very important for admission management and congestion control in the communication network.
利用预测控制方法，设计出一种改进的拥塞控制算法，增强了闭环系统的鲁棒性和稳定性，实现了带宽分配的公平性。
An improved algorithm is presented based on generalized predictive control, which enhances the stability and robustness of closed-loop systems, and realizes the fairness of bandwidth allocation.
提出一种在用户-网络接口处利用对角递归神经网络(DRNN)作为自适应预测器，实现AT M网络自适应拥塞控制的模型。
This paper presents an adaptive congestion control model in ATM networks at the user to network interface by using a diagonal recurrent neural network (DRNN) as an predictor.
当DRNN预测下一时刻缓冲区中的信元数超过阈值时，控制器产生一个反馈控制信号减小信源进入网络的信元速率以避免拥塞发生。
When DRNN predicts that the number of cells in buffer exceeds the threshold limit in the next time cycle, a control signal is generated by the controller to throttle arrival cell rate.
准确的网络态势预测可以有效的减小网络拥塞程度。
The efficient prediction of network situation can reduce the congestion of the network.
准确的网络态势预测可以有效的减小网络拥塞程度。
The efficient prediction of network situation can reduce the congestion of the network.
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