基于神经网络的业务量预测研究

Research on Predicting Network Traffic Using Neural Networks

分别采用back-propagation(BP)算法和Favidon最小二乘学习算法训练神经网络(NN),并用于复杂业务流量预测。以自相似流量模型验证了2种NN学习算法的有效性,并分析比较了他们在流量预测中的可行性,得出Davidon最小二乘学习算法训练的NN比BP算法收敛速度快、收敛误差相差不多,验证了复杂自相似业务流的可预测性,为复杂自相似网络业务流预测的研究提供了一种有效途径。

This paper used Back-propagation（BP） algorithms and Davidon least squaresbased learning algorithm to train the neural network（NN） to predict the nonlinear self-similar network traffic respectively. The feasibility and advantage of these two algorithms were discussed by analyzing the Mean learning errors, training errors and the convergent speed of these two training algorithms. The simulation demonstrated that the NN trained by both of these two training algorithms can well predict this traffic, Compared with BP algorithms, the Davidon least squares-based learning algorithm can converge quickly and has the al- most same prediction accuracy, It supplied a feasible method to predict the complex selfsimilar network traffic.