摘要
目前对混沌时间序列的预测研究大多建立在相空间重构基础之上。然而在重构相空间时,需要选取两个参数即延迟时间与嵌入维数,引入微熵率最小的原则选取这两个参数。在重构相空间后,利用LS-SVR对混沌时间序列进行预测研究。并在MATLAB200b环境下建立混沌时间序列的预测模型。利用Mackey-Glass混沌时间序列与工作面瓦斯涌出量混沌时间序列数据对算法进行验证。结果表明,在熵率最小的原则下确定的嵌入维数与延迟时间其几何意义明确,通过编程实现简单明了.而在此基础上重构的相空间中,利用LS-SVR预测模型的预测效果较好,而对实际现场瓦斯突出在短期内的预测,也得到了较高的精度。
At present nonlinear time series prediction are based on reconstructed phase space.However,only determining the phase space of embedding dimension and delay time,one can reconstruct the time sequence of phase space.To solve this problem this paper first introduces the minimum entropy ratio principle to determine the embedding dimension and delay time,which can determine the embedding dimension and delay time at the same time.Secondly,the phase space can be reconstructed by using the known embedding dimension and delay time.Nonlinear time series can be predicted using well-established LSSVR model in the reconstructed phase space.Finally,in MATLAB200b environment,the algorithm is verified through the Mackey-Glass time-series data and the actual gas emission data.The results show that the geometric meaning is clear and program is simple by minimum entropy ratio principle to determine the embedding dimension and delay time.High time series prediction accuracy is obtained in this reconstructed phase space,and the same time high accuracy also can be obtained in short-term predicting face gas emission.
出处
《微型电脑应用》
2014年第1期31-34,共4页
Microcomputer Applications
关键词
混沌时间序列
相空间重构
预测
Chaotic Time Series
Phase Space Reconstruction
Prediction
