scholarly journals Adaptive predict-filter of chaotic time series constructed Based on the neighbou rhood in the reconstructed phase space(Ⅱ)nonlinear adaptive filter

2003 ◽  
Vol 52 (5) ◽  
pp. 1102
Author(s):  
Gan Jian-Chao ◽  
Xiao Xian-Ci
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Adaptive Filter ◽  
Chaotic Time Series ◽  
Reconstructed Phase Space

Prediction of Chaotic Time Series of Bridge Monitoring System Based on Multi-Step Recursive BP Neural Network

2010 ◽  
Vol 159 ◽  
pp. 138-143 ◽  
Author(s):  
Jian Xi Yang ◽  
Jian Ting Zhou
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Bp Neural Network ◽  
Chaotic Time Series ◽  
Effective Theory ◽  
Maximum Lyapunov Exponent ◽  
Space Correlation ◽  
Bridge Monitoring System ◽  
Monitoring Information

BHM is an important means to assess and predict the safety operation of large bridge in service around the world. Given the missing of real-time monitoring information for some time and the lack of effective theory and technique to capture the missing information and even to predict the evolution of structure, this paper made an attempt to predict the evolution of monitoring information using time series and chaotic theory. Firstly, maximum Lyapunov exponent of available monitoring information is calculated to assess the chaos of the bridge structure. The parameters of reconstructed phase space, correlation dimension and time delay, are calculated by C-C algorithm and G-P algorithm respectively. According to empirical formula, one 3-layer BP neural network is established Ten recursions are carried out. The results show that multi-layer recursive BP neural network is able to predict BHM information. Using chaotic time series to reconstruct phase space and applying multi-layer recursive BP neural network to predict BHM information facilitates further estimation and prediction of bridge safety condition by means of chaotic nonlinear characteristics.

Dynamics of Thermoacoustic Oscillations Leading to Lean Flame Blowout

2012 ◽  
Author(s):  
Lipika Kabiraj ◽  
R. I. Sujith
Keyword(s):  
Time Series ◽  
Steady State ◽  
Phase Space ◽  
Dynamical Systems ◽  
Nonlinear Oscillation ◽  
Bifurcation Analysis ◽  
Nonlinear Time Series Analysis ◽  
Pressure Oscillations ◽  
Reconstructed Phase Space ◽  
Thermoacoustic Oscillations

Lean flame blowout induced by thermoacoustic oscillations is a serious problem faced by the power and propulsion industry. We analyze a prototypical thermoacoustic system through systematic bifurcation analysis and find that starting from a steady state, this system exhibits successive bifurcations resulting in complex nonlinear oscillation states, eventually leading to flame blowout. To understand the observed bifurcations, we analyze the oscillation states using nonlinear time series analysis, particularly through the representation of pressure oscillations on a reconstructed phase space. Prior to flame blowout, a bursting phenomenon is observed in pressure oscillations. These burst oscillations are found to exhibit similarities with the phenomenon known as intermittency in the dynamical systems theory. This investigation based on nonlinear analysis of experimentally acquired data from a thermoacoustic system sheds light on how thermoacoustic oscillations lead to flame blowout.

Phase-space prediction of chaotic time series

1998 ◽  
Vol 13 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Dejin Yu ◽  
Weiping Lu ◽  
Robert G Harrison
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Chaotic Time Series

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.

Sign in / Sign up

Export Citation Format

Share Document