scholarly journals Evaluation on Hysteresis Model and Dynamic Deformation Properties of Soil by Prediction Method of Chaotic Time Series.

1997 ◽  
pp. 37-47
Author(s):  
Ikutaro Kumazaki ◽  
Eiichi Watanabe ◽  
Hitoshi Furuta ◽  
Hiromichi Kondo ◽  
Takeshi Sugiyama
Keyword(s):  
Time Series ◽  
Dynamic Deformation ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Hysteresis Model ◽  
Deformation Properties ◽  
Properties Of Soil

scholarly journals A novel multi-step adaptive prediction method for chaotic time series

2006 ◽  
Vol 55 (4) ◽  
pp. 1666
Author(s):  
Meng Qing-Fang ◽  
Zhang Qiang ◽  
Mu Wen-Ying
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Adaptive Prediction

Local Piecewise-Linearity Prediction Method of Chaotic Time Series

2013 ◽  
Vol 712-715 ◽  
pp. 2415-2418
Author(s):  
Juan Liu ◽  
Xue Wei Bai ◽  
Dao Cai Chi
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Local Region ◽  
Liaoning Province ◽  
Largest Lyapunov Exponent ◽  
Region Method ◽  
Local Prediction ◽  
Piecewise Linearity

A Local Piecewise-Linearity Prediction method is presented, Based on the advantages and limitations of local prediction of chaotic time series. Taking time series of rainfall as example for prediction the rainfall of one city in Liaoning province, which includes the application of the largest Lyapunov exponent, Local-region method and Local Piecewise-Linearity method. The method proposed is proved practical in comparison with the observed data.

Chaotic Time Series Prediction Algorithm for Lorenz System

2014 ◽  
Vol 513-517 ◽  
pp. 2412-2415
Author(s):  
Chen Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Lorenz System ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

Based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm for BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to generate better network initial thresholds and weights so as to compensate the random defects for the thresholds and weights of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series generated by Lorenz system. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so prove it is feasible and effective in the chaotic time series.

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