scholarly journals Local Polynomial Coefficient AR Prediction Model for Chaotic Time Series

2015 ◽  
Vol 04 (02) ◽  
pp. 56-69
Author(s):  
相武 彭
1998 ◽  
Vol 58 (2) ◽  
pp. 2640-2643 ◽  
Author(s):  
A. K. Alparslan ◽  
M. Sayar ◽  
A. R. Atilgan

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

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Liyun Su ◽  
Chenlong Li

We apply the polynomial function to approximate the functional coefficients of the state-dependent autoregressive model for chaotic time series prediction. We present a novel local nonlinear model called local polynomial coefficient autoregressive prediction (LPP) model based on the phase space reconstruction. The LPP model can effectively fit nonlinear characteristics of chaotic time series with simple structure and have excellent one-step forecasting performance. We have also proposed a kernel LPP (KLPP) model which applies the kernel technique for the LPP model to obtain better multistep forecasting performance. The proposed models are flexible to analyze complex and multivariate nonlinear structures. Both simulated and real data examples are used for illustration.


2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Congqing Wang ◽  
Linfeng Wu

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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44288-44299
Author(s):  
Mingyang Lv ◽  
Xiaogang Zhang ◽  
Hua Chen ◽  
Chuanwu Ling ◽  
Jianmin Li

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