Railway coal dispatched volume prediction based on maximum Lyapunov exponent

被引：0

作者：

Wu, Hua-Wen ^{[1
]}

Wang, Fu-Zhang ^{[1
]}

机构：

[1] Institute of Computing Technology, China Academy of Railway Sciences, Beijing 100081, China

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2013年 / 13卷 / 06期

关键词：

Time series - Lyapunov functions - Phase space methods - Forecasting - Chaos theory - Coal transportation - Differential equations - Neural networks - Lyapunov methods - Railroads;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The phase space reconstruction method of chaos theory is used to analyze the three groups of time series associated with railway coal dispatched volume. The embedded time-delay, embedded dimension, correlation dimension and the maximum Lyapunov exponent of each time series are separately calculated. The results are used to judge the chaotic characteristic of time series. The analytical results show as follows: the growth amount and growth rate of railway coal dispatched volume have chaotic characteristics while the coal dispatched volume doesn't. The maximum Lyapunov exponent method and BP neural network are separately used to forecast the growth amount and growth rate of railway coal dispatched volume. The result shows that the predicted data using maximum Lyapunov exponent method is anastomotic with the real data. The maximum Lyapunov exponent method is better than BP neural network in prediction. The maximum Lyapunov exponent prediction of chaos theory has extensive and practical value in railway coal dispatched volume time series prediction.

引用

页码：184 / 190