Study of the Safety Assessment Model of Coal Mine Based on BP Neural Network

2009 ◽  
Author(s):  
Yongjian Fan ◽  
Jianying Mai ◽  
Yanguang Shen
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Assessment Model

Safety Assessment of the General Overhead Traveling Crane Metal Structure Based on BP Neural Network

2011 ◽  
Vol 101-102 ◽  
pp. 15-20 ◽  
Author(s):  
Ge Ning Xu ◽  
Qian Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Safety Assessment ◽  
Safety Evaluation ◽  
Metal Structure ◽  
Assessment Model ◽  
Security Model ◽  
Network Training ◽  
Training Samples

Safety assessment of bridge crane metal structure is widely needed. A general bridge safety assessment model of metal structure based on BP neural network is established. BP neural network is suitable for the problem that is not fully known and the adaptability of the dynamic system, and can facilitate the assignment and statistics of the safety evaluation system. Matlab7.0 software is used for the network training process. Through the training, samples to be tested were verified for the feasibility of the security model. The security model based on BP neural network for the general overhead traveling crane structure could provide a safety assessment and evaluation methods.

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.

Study on the Discrimination Method of Driving Safety States Based on BP Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 6111-6114
Author(s):  
Feng Ping Cao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Driving Safety ◽  
Assessment Index ◽  
Evaluation Indexes ◽  
The Neural Network ◽  
Discrimination Model ◽  
Assessment Index System ◽  
Discrimination Method

In order to estimating the state of driving safety and reducing accidents, a discrimination method of driving safety states based on BP neural network was presented in the paper. Firstly, the influencing factors on the vehicle driving safety were analyzed, and ten main factors that affected the driving safety of vehicles were confirmed, which constitute the safety assessment index system for vehicle driving. Then the discrimination model of driving safety states based on BP neural network was established, and inputs and outputs for the neurons were determined. At last, the input data for neurons were acquired on the basic of the main evaluation indexes of vehicle driving safety, and these data were used to train the neural network. The training result conform to expectations of the training requires.

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.

Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 528-533
Author(s):  
Yuan Sheng Huang ◽  
Wei Fang ◽  
Cheng Fang Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Transmission Grid ◽  
Comprehensive Evaluation Method ◽  
System Method ◽  
Grid Based

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

scholarly journals Study on Evaluation Model of Emergency Rescue Capability of Chemical Accidents Based on PCA-BP

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jianghong Liu ◽  
Junfeng Wu ◽  
Weisi Liu
Keyword(s):  
Neural Network ◽  
Emergency Management ◽  
Bp Neural Network ◽  
Index System ◽  
Evaluation Model ◽  
Principal Component ◽  
Assessment Model ◽  
Chemical Accidents ◽  
Emergency Rescue ◽  
Model Based

The emergency management of chemical accidents plays an important role in preventing the expansion of chemical accidents. In recent years, the evaluation and research of emergency management of chemical accidents has attracted the attention of many scholars. However, as an important part of emergency management, the professional rescue team of chemicals has few evaluation models for their capabilities. In this study, an emergency rescue capability assessment model based on the PCA-BP neural network is proposed. Firstly, the construction status of 11 emergency rescue teams for chemical accidents in Shanghai is analyzed, and an index system for evaluating the capabilities of emergency rescue teams for chemicals is established. Secondly, the principal component analysis (PCA) is used to perform dimension reduction and indicators’ weight acquisition on the original index system to achieve an effective evaluation of the capabilities of 11 rescue teams. Finally, the indicators after dimensionality reduction are used as the input neurons of the backpropagation (BP) neural network, the characteristic data of eight rescue teams are used as the training set, and the comprehensive scores of three rescue teams are used for verifying the generalization ability of the evaluation model. The result shows that the proposed evaluation model based on the PCA-BP neural network can effectively evaluate the rescue capability of the emergency rescue teams for chemical accidents and provide a new idea for emergency rescue capability assessment.

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