A Chaos Time Series Prediction Method of Generalized Phase Space

被引：0
|
作者：
Pan, Yu-min [1 ,2 ]
Deng, Yong-hong [1 ,2 ]
Wu, Li-feng [1 ,2 ]
Zhang, Quan-zhu [1 ,2 ]
机构：
[1] North China Inst Sci & Technol Beijing, Informat & Control Technol Inst, Beijing 101601, Peoples R China
[2] Capital Normal Univ, Coll Informat Engn, Beijing 100048, Peoples R China
来源：
PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INFORMATION ENGINEERING (ICACIE) | 2016年 / 64卷
关键词：
Chaotic; Phase space reconstruction; RBF; Distribution coefficient; BP; Gas emission; Wavelet denoising;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Aiming at the limitation of the regression prediction method for mine gas emission, a chaotic time series prediction method for reconstruction of generalized phase space is proposed in this paper. This method is used to construct phase spaces for time series with unobvious chaotic characteristics and fit equivalent chaotic attractors by RBF and BP network in order for prediction researches by calculating saturated embedding dimensions and designing main neural network parameters. A prediction method for stabilizing the neural network prediction result is brought forward and wavelet denoising is made for original signals to improve the prediction precision. This method can effectively overcome the deficiency of record data types, with stronger practicability, then the simulation experiment on Mackey-Glass time series and actual mine gas emission has proved the feasibility and effectiveness of this method.
引用
收藏
页码：44 / 49
页数：6
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