scholarly journals Time Series Prediction of Wireless Network Traffic Flow Based on Wavelet Analysis and BP Neural Network

2020 ◽  
Vol 1533 ◽  
pp. 032098 ◽  
Author(s):  
Yanchi Li ◽  
Jin Huang ◽  
Haojie Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wavelet Analysis ◽  
Traffic Flow ◽  
Wireless Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Time Series Prediction

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.

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.

Research on the Magnitude Time Series Prediction Based on Wavelet Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 233-236 ◽  
Author(s):  
Yan Lan Chen ◽  
Yi Chen ◽  
Qing Huang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Predictive Performance ◽  
Wavelet Neural Network ◽  
Single Step ◽  
Reconstructed Phase Space ◽  
Takens Theorem

Based on the fundamental principles of the wavelet analysis combining with BP neural network, the paper can obtain the minimum embedding dimension and delay time. According to the chaos theory, the phase space of the magnitude time series can be reconstructed by Takens theorem. The paper uses wavelet neural network to train and test the nonlinear magnitude time series in the reconstructed phase space. The simulation results show that the predictive effect of the magnitude time series is remarkable and the predictive performance of single-step prediction is superior to that of multi-step prediction.

scholarly journals Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

2018 ◽  
Vol 218 ◽  
pp. 03021 ◽  
Author(s):  
Bagus Priambodo ◽  
Yuwan Jumaryadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Historical Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Traffic Data ◽  
Data Traffic ◽  
Neural Network Models

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

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.

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