Non-contact electrical resistivity measuring system based on BP neural network

2018 ◽  
Author(s):  
Chunting Liu ◽  
Xiaotao Han ◽  
Qi Chen ◽  
Zelin Wu ◽  
Daikun Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Electrical Resistivity ◽  
Bp Neural Network ◽  
Measuring System

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.

scholarly journals Accurate Adaptive Compensation Method for Mechanical Structure Error of the Blade Measuring System

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wei Shao ◽  
Peng Peng ◽  
Awei Zhou ◽  
Quanquan Zhu ◽  
Di Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Laser Interferometer ◽  
Compensation Method ◽  
Measuring System ◽  
Adaptive Compensation ◽  
Mechanical Structure ◽  
Compensation Methods ◽  
Theoretical Significance

In view of the high precision requirement for mechanical structure of aeronautical blade measuring system, this paper proposes a laser interferometer to measure the error of the spatial nodes of the measuring system based on a comprehensive analysis of domestic and foreign error compensation methods for the measuring system. The optimized algorithm backpropagation (BP) neural network (OA-BPNN) compensation method is utilized to adaptively compensate for the systematic error of the mechanical system. Compared with the traditional polynomial fitting and genetic algorithm BP neural network (GA-BPNN) algorithm, the results show that the OA-BPNN algorithm is characterized by the best adaptability, precision, and efficiency for the adaptive error compensation. The spatial errors in the XYZ directions are reduced from 10.9, 60.1, and 84.2 μm to 1.3, 4.0, and 2.4 μm, respectively. The method is of great theoretical significance and practical value.

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

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