A Chaos Time Series Prediction Method of Generalized Phase Space
- DOI
- 10.2991/icacie-16.2016.11How to use a DOI?
- Keywords
- Chaotic, Phase space reconstruction, RBF, Distribution coefficient, BP, Gas emission, Wavelet denoising
- Abstract
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.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yu-min Pan AU - Yong-hong Deng AU - Li-feng Wu AU - Quan-zhu Zhang PY - 2016/10 DA - 2016/10 TI - A Chaos Time Series Prediction Method of Generalized Phase Space BT - Proceedings of the 2016 International Conference on Automatic Control and Information Engineering PB - Atlantis Press SP - 44 EP - 49 SN - 2352-5401 UR - https://doi.org/10.2991/icacie-16.2016.11 DO - 10.2991/icacie-16.2016.11 ID - Pan2016/10 ER -