scholarly journals Prediction of the First Weighting from the Working Face Roof in a Coal Mine Based on a GA-BP Neural Network

2019 ◽  
Vol 9 (19) ◽  
pp. 4159
Author(s):  
Tan ◽  
Yang ◽  
Chang ◽  
Zhao
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Initial Pressure ◽  
Mining Area ◽  
Mine Pressure ◽  
Mine Production ◽  
Production Safety ◽  
Matlab Programming

The accidents caused by roof pressure seriously restrict the improvement of mines and threaten production safety. At present, most coal mine pressure forecasting methods still rely on expert experience and engineering analogies. Artificial neural network prediction technology has been widely used in coal mines. This new approach can predict the surface pressure on the roof, which is of great significance in coal mine production safety. In this paper, the mining pressure mechanism of coal seam roofs is summarized and studied, and 60 sets of initial pressure data from multiple working surfaces in the Datong mining area are collected for gray correlation analysis. Finally, 12 parameters are selected as the input parameters of the model. Suitable back propagation (BP) and GA(genetic algorithm)-BP initial roof pressure prediction models are established for the Datong mining area and trained with MATLAB programming. By comparing the training results, we found that the optimized GA-BP model has a larger determination coefficient, smaller error, and greater stability. The research shows that the prediction method based on the GA-BP neural network model is relatively reliable and has broad engineering application prospects as an auxiliary decision-making tool for coal mine production safety.

scholarly journals A New Approach of Disaster Forecasting Based on Least Square Optimized Neural Network

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Fanbao Meng ◽  
Suolin Jing ◽  
Xizhen Sun ◽  
Changxiang Wang ◽  
Yanbo Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rock Burst ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Least Square Method ◽  
Mining Area ◽  
Least Square ◽  
Control Factors ◽  
General Law ◽  
The Impact

The evaluation of the risk is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst, and the research on the fast forecast at scene of the rock burst is more important for the safety production of coal mine. Aiming at the problem that dynamic disasters caused by many factors and heterogeneity of coal and rock are difficult to predict in the process of coal mining, in this paper, the general law and the risk control factors of the rock burst are studied, a mathematical model based on the BP neural network was built according to the different actual mining conditions in the mining area, and the output layer has obtained the prediction result. Then, the results of the output samples after training were fitted by using SPSS software, and the fitting function was obtained by multiple least square fitting. Finally, the fitting results are checked by the data of actual coal mine dynamic disaster parameters. The prediction results show that the simulation results of BP neural network prediction model and the fitting function of the least square method can reduce the impact of subjective judgment on the prediction results, and the application of the fitting function can obtain the prediction results in the first time to ensure the construction safety. The method of on-site hazard assessment and inspection by using fitting function is simple and feasible and has high accuracy, which provides a new idea for the field prediction of rock burst.

Research on Forecasting of Airflow Temperature in Heading Face Based on the GA-BP Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 668-672
Author(s):  
Xiang Zhang ◽  
Bai Shun Wang ◽  
Shuo Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
High Prediction ◽  
Bp Neural Network Model ◽  
Matlab Programming

In order to solve the problem of forecasting airflow temperature in heading face, a new model of forecasting airflow temperature in heading face with Matlab programming is built on the BP neural network model, making use of genetic algorithms to optimize the initial weights and thresholds of the network. According to the analysis of test carried out in a coal mine in Huainan, the results show that the model of fast convergence and high prediction accuracy is one of the most effective ways of forecasting airflow temperature in heading face.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

Research on Traffic Delay Predicting at Signalized Intersection Based on Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 901-904 ◽  
Author(s):  
Chang Jiang Zheng ◽  
De Gang Lin ◽  
Shu Kang Zheng ◽  
Shu Yan Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Signalized Intersection ◽  
Actual Data ◽  
Traffic Delays ◽  
Traffic Delay ◽  
Strong Ability

Available traffic delays prediction models for signalized intersection tend to predict the traffic delays under certain conditions and they are weak in adapt to different situation. In the paper, based on the theories of BP neural network, a network model, having a strong ability to adapt to different conditions, for traffic delay in average hours at a signalized intersection is established. It is trained and tested utilizing the data of traffic delay in average hours at a certain entrance of a signalized intersection. The predicted results and the actual data are compared with each other and the results prove the reliability and effectiveness of BP neural network in predicting traffic delays.

Paste-filling weighing control system optimization based on neural network

2017 ◽  
Vol 14 (2) ◽  
pp. 155-158 ◽  
Author(s):  
Guimei Wang ◽  
Yong Shuo Zhang ◽  
Lijie Yang ◽  
Shuai Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Back Propagation ◽  
System Optimization ◽  
Filling Material ◽  
Content Type ◽  
Control Precision ◽  
Filling System

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.

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