scholarly journals An Improved BP Neural Network Algorithm for Prediction of Roadway Support

2021 ◽  
Vol 15 ◽  
pp. 393-399
Author(s):  
Yan-Jun He ◽  
Jin-shan Zhang ◽  
Chao-Gang Pan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Engineering Practice ◽  
Related Factors ◽  
Support Design ◽  
Network Training ◽  
Coal Roadway ◽  
Allowable Range ◽  
Roadway Support ◽  
New Research

Based on the engineering practice and the research and analysis of the knowledge in the field of roadway support, the paper puts forward to use an improved BP neural network to study the supporting types by the investigation, and obtained the related factors of the supporting types of the mining roadway and the successful reinforcement cases of the roadway. The proposed algorithm is applied to the prediction of coal roadway support parameters, and the main influencing factors of coal roadway support design are determined. From the typical engineering cases of roadway support collected on site as neural network training samples, the forecasting model of support parameters is established. Through the experimental data and simulation results, it can be seen that both the error convergence process and results of convergence speed, convergence accuracy and support types are ideal, the prediction error is within the allowable range, and the prediction accuracy is high, which verifies the reliability of this method and provides a new research idea and good application value for the study of support types of mining roadway.

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

Data Mining with BP Neural Network Algorithm Based MapReduce

2013 ◽  
Vol 380-384 ◽  
pp. 2915-2919 ◽  
Author(s):  
Jian Ming Cui ◽  
Yan Xin Ye
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Resource Constraints ◽  
Training Time ◽  
Computing Platform ◽  
Network Training ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Cloud Computing Platform

Traditional massive data mining with BP neural network algorithm, resource constraints of the ordinary stand-alone platform and scalability bottlenecks and classification process serialization due to classification inefficient results, and also have an impact on the classification accuracy. In this paper, the Detailed description of the flow of execution of the BP neural network parallel algorithm in Hadoop's MapReduce programming model.Experimental results show that: the BP neural network under the cloud computing platform can greatly shorten the network training time, better parallel efficiency and good scalability.

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Training ◽  
Gradient Descent Algorithm ◽  
Ipso Algorithm

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

Selection and Prediction of Earthquake Casualties Related Factors Based on BP Neural Network Analysis

2014 ◽  
Vol 543-547 ◽  
pp. 2084-2088 ◽  
Author(s):  
Run Biao Bao ◽  
Man Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Related Factors ◽  
Value Model ◽  
Correlation Factors ◽  
Relevant Factors ◽  
Earthquake Casualties

To reduce the prediction error rate of earthquake casualties, the paper proposed a prediction model with two steps: (1) screening of the earthquake casualties correlation factors; (2) improving the predictive veracity of general BP(Back Propagation) neural network model.By the analysis of 9 kinds of correlation factors, the paper established the MIV(Mean Impact Value) model based on BP neural network to screen the final correlation factors, and the paper got 6 main correlation factors according to the size of output weights of the factors. Finally, the paper verified the accuracy and practicability of the model through the validation of the model and the solving of prediction error of relevant factors hasn't been selected.

Research on configuration design and operation effect evaluation for ultra high voltage (UHV) vertical insulator cleaning robot

2019 ◽  
Vol 47 (1) ◽  
pp. 90-101 ◽  
Author(s):  
Yu Yan ◽  
Wei Jiang ◽  
An Zhang ◽  
Qiao Min Li ◽  
Hong Jun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Motion Planning ◽  
High Voltage ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Effect Evaluation ◽  
Content Type ◽  
Network Training ◽  
Cleaning Robot ◽  
Physical Prototype

Purpose This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning. Design/methodology/approach The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed. Findings Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization. Originality/value An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.

The Research of the Water Quality Prediction Model for the Circulating Cooling Water System

2013 ◽  
Vol 385-386 ◽  
pp. 408-411
Author(s):  
Qiang Gao ◽  
Tian Lu Ma ◽  
Jun Fang Li ◽  
Chen Guang Li
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Water System ◽  
Cooling Water ◽  
Water Quality Prediction ◽  
Network Training ◽  
Prediction Function ◽  
Circulating Cooling Water

Aiming at the common quality faults of scaling and corrosion in circulating cooling water, water quality index were often used to determine the scaling and corrosion of circulating cooling water quality trends. Prediction model of corrosion and scaling rate was built based on BP Neural Network in this paper. The optimal initial individuals were written into the network operating system to optimize the disadvantages of weights and thresholds in BP neural network based on genetic algorithm. The prediction function would output after the network training after comparison of predicted and actual values of the model. The performance of the actual situation was verified to match the model prediction.

Application of Artificial Neural Network in GPS Height Transformation

2014 ◽  
Vol 501-504 ◽  
pp. 2162-2165 ◽  
Author(s):  
Bo Fu ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nonlinear Coefficient ◽  
Height Anomaly ◽  
Engineering Practice ◽  
Model Structure ◽  
Normal Height ◽  
Sample Data ◽  
Geodetic Height ◽  
Residual Errors

GPS technology has been widely used since it was put into use. At present, the plane locational accuracy of GPS can already achieve millimeter level. But in terms of height, in order to apply the GPS ellipsoidal height in engineering practice, the geoid seperation or height anomaly of the corresponding point must be achieved to transform GPS geodetic height to normal height. In this paper, by taking 12 points from the national GPS control network of Xuxiang Village of Haining City as sample data, a BP neural network using 3-4-2-1 model structure is adopted and a nonlinear coefficient 1.1 is added in the response function. The height anomalies of the 5 points of the test set are calculated and the residual errors are achieved by comparing with the measured values. The internal and external coincidence accuracies of the model are 0.824cm and 0.922cm separately. The result shows that the model can completely meet the precision requirement of the fourth-grade leveling survey and can be used to transform the heights of the study area.

Defects Rapid Identification of Marking Region Based on Binarization and BP Neural Network

2012 ◽  
Vol 482-484 ◽  
pp. 726-731
Author(s):  
Jian Chen ◽  
Jin Wang ◽  
Guo Dong Lu ◽  
Xiao Ming Jiang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Efficiency ◽  
Rapid Identification ◽  
Final Test ◽  
Domestic Technology ◽  
Network Training ◽  
The Status ◽  
Defects Identification ◽  
Manual Interaction

The status that domestic technology forming the high-pressure gas bottles surface leading to the poor marking quality is analyzed. The rapid image processing method based on Binarization is presented to identify the defects position and profile from the marking region quickly, and it is designed with four steps: CCD capture, pixel enhancement, edge identification and feature extractions. By the statistical analysis of the project practice, the defects is defined as four typical types in shape, and then through the BP neural network training to identify the defects type effectively and driving the machine pre-set coping strategies to make appropriate responses automatically without manual interaction. The final test shows that the automatic high-efficiency marking defects identification is achieved

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.

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