Prediction of Network Delay 网络时延预测
A new multi-branch time delay neural network is adopted to conduct prediction research on chaotic time series.
采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究。
Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizing Bayes' regularization.
基于相空间重构的非线性预报思想，建立一个时滞的BP神经网络模型，采用贝叶斯正则化方法提高BP网络的泛化能力。
The result proves that predicting network time-delay by SVM has greater accuracy than by linear prediction algorithm.
结果证明，和线性预测算法相比，采用支持向量机预测网络延时具有较高的正确率。
应用推荐