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.
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。
The DRNN controller was constructed based on the hysteretic characteristics of the GMA, and on-line learned the inverse hysteresis model of the GMA by the feedback-error learning scheme.
DRNN控制器是根据GMA的滞回特性构造的，通过反馈误差学习方案在线学习GMA的逆滞回模型。
Diagonal recurrent neural network (DRNN) is a modified model of the fully connected recurrent neural network with the advantage in capturing the dynamic behavior of a system.
对角循环神经网络是一类经过修正的全连接循环神经网络，在系统动态行为的俘获方面具有明显的优势。
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