网络流量的神经网络自适应Smith预估补偿控制

网络的时延对基于速率反馈的ATM网络的ABR流量控制具有极大的不利影响，Smith预估补偿是克服大纯滞后影响的有效手段，然而其对时延的预估误差非常敏感，实际网络时延的不确定性往往使其难以取得满意的效果。该文将在线学习的神经网络自适应控制器与Smith预估补偿相结合，很好地克服了网络的时延及其不确定性对流量控制的不利影响，从而使信源的发送速率能快速响应网络状态的变化。与PIDSmith预估补偿控制相比，控制的适应性和鲁棒性更好，更适用于实际网络，且为保证信元不溢出及链路带宽充分利用所需的缓冲容量更低。

Neural Network Adaptive Control with Smith Predictor for Flow Rate in Networks

The network time delay has a great adverse effect on the rate-based ABR flow control in ATM networks. Smith predictor is an effective means that overcomes the large time delay. However, it is very sensitive to model error, so it is not satisfactory to use only Smith predictor in real networks which has indeterminacy of the time delay. The paper designs the neural network adaptive controller with Smith predictor for the flow control, which can overcome the adverse effect caused by the time delay and its indeterminacy well. Thus the source rates can respond to the changes of network status rapidly. Compared with PID Smith predictor control, this scheme has much better adaptability and robustness which are applicable to actual networks, and much lower buffer capacity which is necessary for no cell overflow and link bandwidth full utilization.