小波神经网络在教育网格下行流量预测中的应用

Application of Wavelet Neural Network in Educational Grid Downlink Traffic Prediction

准确预测教育资源网格的下行流量有助于网格的负载均衡和信息安全管理.小波神经网络适合于对具有随机性和不确定性特征的网格下行流量进行建模和非线性预测.针对一般小波神经网络预测模型存在收敛速度较慢,误差较大,稳定性较差等不足,在基于梯度下降法的网络权值和参数修正方案中增加了动量项,同时,提出了一种对预测的中间结果引入随机样本替换机制的改进算法.实验结果表明,该算法能有效降低网络训练的收敛时间,提高网络预测的准确性和稳定性.

Accurate predicted the downlink traffic contributes to traffic load balancing and information security management in educational resources grid. Wavelet neural network is suitable for modeling and nonlinear prediction in grid downlink traffic which has the randomness and uncertainty characteristic. General wavelet neural network prediction model had some defects such as convergence slower, larger error and poor stability. In order to eliminate or improve the existing defects, a momentum was added in the scheme which was used to adjust the network weights and parameters based on gradient descent algorithm, meanwhile, an improved algorithm with random sample replacement mechanism in temporarily prediction results was proposed. Experimental results show that the proposed algorithm can reduce the convergence time in network training and improve the prediction accuracy and stability.