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.

Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization

2014 ◽  
Vol 513-517 ◽  
pp. 1096-1100
Author(s):  
Yue Hou ◽  
Hai Yan Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Connection Weight ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In order to improve the neural network structure and setting method of parameters, based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm of BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to obtain better network initial threshold and weight so as to compensate the defect of connection weight and thresholds choosing of BPNN, thus BPNN can have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series of tent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds 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 for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

Chaotic Time Series Prediction Algorithm for Lori Mapping Based on Optimized BP Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2108-2111
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds 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 for Lori mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

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.

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