关键词: 分式过程神经元网络, 学习算法, 网络流量, 预测

Abstract: To better solve the network traffic prediction problems, according to that the fraction function approximation nature and fitting ability in function approximation are much larger than linear function, and the process neural networks have the ability of non-linear transformation to time-varying function, a fraction process neural network model and its learning algorithm are proposed. The experimental result shows that the network model has flexibility approximation properties for singular value process function and sensitivity reactions near the area in the singular value better than the general process neural network. The model can be trained and be used to forecast flow using network measured data, and achieve good application effect.

Key words: structural formula process neural network, learning algorithm, network traffic, prediction