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.

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.

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.

Improved Dynamic Process Neural Network and its Application

2010 ◽  
Vol 458 ◽  
pp. 143-148
Author(s):  
Pei Yin Zhang ◽  
G.B. Yu ◽  
B. Dai ◽  
Ying Jie Ao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Dynamic Process ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Test Results ◽  
Tourism Demand ◽  
Swarm Optimization ◽  
Proposed Model

The tourism demand is essential in terms of national economy and the improvement of people’ income. But it is difficult for traditional methods to predict the tendency of the tourism demand. In this paper, a time series prediction method based on dynamic process neural network (DPNN) is proposed to solve this problem. An improved particle swarm optimization (IPSO) is developed. By tuning the structure and improving the connection weights of PNN simultaneously, a partially connected DPNN can be obtained. The effectiveness of the proposed DPNN is proved by Henon system. Finally, the proposed DPNN is utilized to predict the tourism demand, and the test results indicate that the proposed model seems to perform well and appears suitable for using as a predictive maintenance tool.

Time series prediction method based on Pearson correlation BP neural network

2018 ◽  
Vol 26 (11) ◽  
pp. 2805-2813
Author(s):  
王 可 WANG Ke ◽  
王慧琴 WANG Hui-qin ◽  
殷 颖 YIN Ying ◽  
毛 力 MAO Li ◽  
张 毅 ZHANG Yi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Pearson Correlation ◽  
Time Series Prediction ◽  
Prediction Method

Time Series Forecasting of Tunnel Surrounding Rock Displacement

2011 ◽  
Vol 261-263 ◽  
pp. 1789-1793 ◽  
Author(s):  
Guang Xiang Mao ◽  
Yuan You Xia ◽  
Ling Wei Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
Rock Stress ◽  
Rock Types ◽  
Network Time ◽  
Tunnel Depth

In the process of tunnel construction, because the rock stress redistribute, the vault and the two groups will generate displacement constantly. This paper adopts the genetic algorithm to optimize the weight and threshold of BP neural network, taking the tunnel depth, rock types and part measured values of displacement as input parameters to construct a neural network time series prediction model of tunnel surrounding rock displacement. The method proposed in the paper has been applied in the Ma Tou Tang tunnel construction successfully, and the results show that the model can predict the displacement of the surrounding rock quickly and accurately.

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