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.

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsong Tu ◽  
Yuanzhen Liu ◽  
Ming Zhou ◽  
Ruixia Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Compressive Strength ◽  
Thermal Insulation ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Training Sample ◽  
Recycled Aggregate ◽  
Generalization Performance ◽  
Content Type

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Research on Hydraulic Control System of Straightening Machine with Nine Rolls for Plates Based on BP Neural Network PID Controller

2013 ◽  
Vol 820 ◽  
pp. 117-121 ◽  
Author(s):  
Song Li ◽  
Jin Chun Song ◽  
Guan Gan Ren ◽  
Yan Cai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Mechanical Transmission ◽  
Hydraulic Control ◽  
Control Precision ◽  
Transmission Equipment ◽  
High Control ◽  
Neural Network Pid

A mechanical transmission equipment of traditional straightening machine for plates are driven by worm gear and worm, which causes small straightening force, slow pressing speed and low control precision. However, screwdown control system of straightening machine can be driven by hydraulic system, which will lead to large straightening force, rapid pressing speed and high control precision. This system was designed for straightening machine with nine rolls for plates, its transfer function was deduced, and the analysis on its stability and time response was conducted. A BP neural network PID controller was utilized in the system for improving dynamic characteristics. It can be concluded that the system responds rapidly, and stability and control precision are high if BP neural network PID controller is used in the system.

Estimation of construction project building cost by back-propagation neural network

2019 ◽  
Vol 18 (3) ◽  
pp. 601-609
Author(s):  
Qinghua Jiang
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Estimation Accuracy ◽  
Average Error ◽  
Estimation Model ◽  
Content Type ◽  
Building Cost

Purpose Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units. Design/methodology/approach This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples. Findings According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation. Originality/value The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.

The Design of Intelligence Anti-Running System Based Onthe Inclined Roadway

2014 ◽  
Vol 962-965 ◽  
pp. 3054-3058
Author(s):  
Tai Hua Wang ◽  
Zhi Fu Chen
Keyword(s):  
Neural Network ◽  
Control System ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Safe Operation ◽  
Influence Of Temperature ◽  
Set Point ◽  
Running System

Preventing tramcar accidents is very important in the production of the coal mine. In this paper, S7-200PLC and BP neural network are used to design a intelligent flexible anti-running system. The control system adopts normally closed and flexible bufferred barrier. Following is the simulation experiment scheme. The BP neural network is adopted to eliminate the influence of temperature and filter by photo-electric coupler, which can measure the accurate and reliable tramcar speed. If the speed is faster than set point, the system will give an alarm and barrier does not work ,which stops the harvesters continuing running. This method guarantees the safe operation of coal mine.

Adaptive control of missile attitude based on BP–ADRC

2020 ◽  
Vol 92 (10) ◽  
pp. 1475-1481
Author(s):  
Haiyan Qiao ◽  
Hao Meng ◽  
Wei Ke ◽  
Quanxi Gao ◽  
Shaobo Wang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Control Method ◽  
Extended State ◽  
Dynamics Model ◽  
Closed Loop System ◽  
Content Type

Purpose To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed. Design/methodology/approach ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given. Findings The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error. Practical implications BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible. Originality/value Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

Three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA

2019 ◽  
Vol 36 (6) ◽  
pp. 2066-2083 ◽  
Author(s):  
Xiaohong Lu ◽  
Yongquan Wang ◽  
Jie Li ◽  
Yang Zhou ◽  
Zongjin Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Measuring System ◽  
Content Type ◽  
Coordinate Measurement ◽  
World Coordinate System ◽  
Measurement Algorithm ◽  
Three Dimensional Coordinate

Purpose The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high. Design/methodology/approach A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points. Findings The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points. Originality/value A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

Modeling of underwater wet welding process based on visual and arc sensor

2014 ◽  
Vol 41 (3) ◽  
pp. 311-317 ◽  
Author(s):  
Bo Chen ◽  
Jicai Feng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Weld Seam ◽  
Back Propagation ◽  
Welding Process ◽  
Welding Quality ◽  
Welding Seam ◽  
Content Type ◽  
Underwater Welding ◽  
Underwater Wet Welding

Purpose – The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality. Design/methodology/approach – Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process. Findings – Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding. Originality/value – A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

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