摘要
相空间重构是进行混沌时间序列分析与预测的基础。本文基于混沌理论中相空间重构的两个关键参数嵌入维数和延迟时间相关的观点,采用C-C算法计算嵌入维数和延迟时间,进而对混沌时序进行相空间重构,然后运用改进后的加权一阶局域预测模型进行预测。通过对Lorenz混沌系统和Rossler混沌系统的仿真预测,表明用C-C算法计算嵌入维数和延迟时间,具有操作简便,速度快的优点,利用本文提出的预测模型进行仿真预测,进一步说明本文提出的预测方法可操作性强,对于混沌系统的短期预测有较好的效果。
The phase space reconstruction is the basis of the chaotic time series analysis and prediction. On the view of Embedding dimension and delay time is associated, the C-C algorithm is applied to calculate the embedding dimension and delay time, thus the chaotic time series phase space reconstruction, and then use the improved weighted one-rank local method prediction model to predict. Lorenz chaotic system and Rossler chaotic system simulation predicted that the embedding dimension and delay time with the C-C algorithm has the advantages of simple, fast, and the proposed prediction model simulation predictions further shows that the proposed prediction the method is feasible and has a good effect on the short-term prediction of chaotic systems.
出处
《软件》
2013年第4期34-37,共4页
Software
基金
教育部人文社会科学研究青年基金项目(11YJCZH202)资助
关键词
计算机应用技术
混沌时序
C—C算法
加权一阶局域法
预测
computer application
Chaotic time series
C-C algorithm
Weighted one-rank local method
Prediction
