混沌一支持向量机回归在流量预测中的应用研究

为了提高流量预测准确性,将混沌理论和支持向量机回归应用于网络流量预测.采用相空间重构理论计算实际流量的延时、嵌入维数和Lyapunov指数,证实网络流量存在混沌现象;据此建立混沌-支持向量机预测模型并确定训练样本对,对实际网络流量教据进行预测.结果表明,该方法能有效地进行流量预测,相对于BP神经网络方法,该方法具有更好的预测精度.

Application of Chaos-support Vector Machine Regression in Traffic Prediction

A traffic forecasting model based on the support vector machine(SVM) and chaos was developed to improve the accuracy of the traffic prediction.Based on the phase space reconstruction,it calculates the real-time traffic's delay time,embedded dimension and Lyapunov exponent,and proves that the traffic chaos phenomena exists.That a chaos-SVM model was constructed and pairs of training samples was determined to forecast the real network traffic.The results show that the chaos-SVM model is able to predict networ...