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.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Selection of Neural Network for Crime Time Series Prediction by Virtual Leave-One-Out Tests

2019 ◽  
pp. 117-133
Author(s):  
Stanisław Jankowski ◽  
Zbigniew Szymański ◽  
Zbigniew Wawrzyniak ◽  
Paweł Cichosz ◽  
Eliza Szczechla ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Virtual Leave One Out ◽  
Leave One Out ◽  
Selection Of
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