State-space prediction model for chaotic time series

1998 ◽  
Vol 58 (2) ◽  
pp. 2640-2643 ◽  
Author(s):  
A. K. Alparslan ◽  
M. Sayar ◽  
A. R. Atilgan
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
State Space ◽  
Chaotic Time Series

An Improved Local Weighted Linear Prediction Model for Chaotic Time Series

2014 ◽  
Vol 31 (2) ◽  
pp. 020503 ◽  
Author(s):  
Jian-Ling Qu ◽  
Xiao-Fei Wang ◽  
Yu-Chuan Qiao ◽  
Feng Gao ◽  
Ya-Zhou Di
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Linear Prediction ◽  
Chaotic Time Series ◽  
Linear Prediction Model

scholarly journals Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Congqing Wang ◽  
Linfeng Wu
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Prediction Model ◽  
Dynamic Model ◽  
Chaotic Time Series ◽  
Joint Torque ◽  
Support Vector ◽  
Space Manipulator ◽  
Chaotic Vibration ◽  
Vibration Prediction

The dynamic model of a planar free-floating flexible redundant space manipulator with three joints is derived by the assumed modes method, Lagrange principle, and momentum conservation. According to minimal joint torque’s optimization (MJTO), the state equations of the dynamic model for the free-floating redundant space manipulator are described. The PD control using the tracking position error and velocity error in the manipulator is introduced. Then, the chaotic dynamic behavior of the manipulator is analyzed by chaotic numerical methods, in which time series, phase plane portrait, Poincaré map, and Lyapunov exponents are used to analyze the chaotic behavior of the manipulator. Under certain conditions for the joint torque optimization and initial values, chaotic vibration motion of the space manipulator can be observed. The chaotic time series prediction scheme for the space manipulator is presented based on the theory of phase space reconstruction under Takens’ embedding theorem. The trajectories of phase space can be reconstructed in embedding space, which are equivalent to the original space manipulator in dynamics. The one-step prediction model for the chaotic time series and the chaotic vibration was established by using support vector regression (SVR) prediction model with RBF kernel function. It has been proved that the SVR prediction model has a good performance of prediction. The experimental results show the effectiveness of the presented method.

scholarly journals An Accurate Online Prediction Model for Kiln Head Temperature Chaotic Time Series

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44288-44299
Author(s):  
Mingyang Lv ◽  
Xiaogang Zhang ◽  
Hua Chen ◽  
Chuanwu Ling ◽  
Jianmin Li
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Chaotic Time Series ◽  
Online Prediction ◽  
Head Temperature

The Combinatorial Prediction about Chaotic Times Series of Natural Circulation Flow under Rolling Motion

2014 ◽  
Vol 989-994 ◽  
pp. 1348-1351 ◽  
Author(s):  
Can Yuan ◽  
Qi Cai ◽  
Gang Liu ◽  
Feng Yan
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Natural Circulation ◽  
Chaotic Time Series ◽  
Volume Flow ◽  
Rolling Motion ◽  
Reconstruction Technique ◽  
History Data ◽  
Flow Prediction ◽  
Relationship Of

The paper has established a combinatorial prediction model of chaotic time series based on history data and coupling data. Through the study of the flow characteristic about natural circulation under rolling motion, the single variable reconstruction and coupling multivariate reconstruction are discussed for chaotic time series based on phase space reconstruction technique, and the combinatorial prediction model has been built which bases on developing trend of history data and coupling relationship of correlative data. The paper also studied an example of coolant volume flow prediction with a relative precision of 0.9804 with the established model. The result indicated that the model with high precision and robustness could apply for natural circulation coolant volume flow prediction under rolling motion.

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