Error compensation of photoelectric encoder based on improved BP neural network

2012 ◽  
Author(s):  
Xiao-gang Wang ◽  
Tao Cai ◽  
Fang Deng ◽  
Li-shuang Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Photoelectric Encoder

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

2015 ◽  
Vol 740 ◽  
pp. 120-126
Author(s):  
Zhi Peng Zhang ◽  
Kang Liu ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Machine Tool ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Thermal Error ◽  
Support Vector ◽  
Experiment Research ◽  
Further Development ◽  
Thermal Error Compensation

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

Error compensation based on BP neural network for airborne laser ranging

Optik ◽  
2016 ◽  
Vol 127 (8) ◽  
pp. 4083-4088 ◽  
Author(s):  
Bing Wu ◽  
Shaojun Han ◽  
Jin Xiao ◽  
Xiaoguang Hu ◽  
Jianxin Fan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Laser Ranging ◽  
Airborne Laser

Study on Machine Accuracy of the Serial-Parallel Machine Tool Based on the BP Neural Network

2009 ◽  
Vol 407-408 ◽  
pp. 140-145
Author(s):  
Xu Ming Pei ◽  
Jie Liu ◽  
Chao Zhang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Degrees Of Freedom ◽  
Simulation Analysis ◽  
Control Strategies ◽  
Parallel Machine ◽  
Machine Accuracy ◽  
Parallel Machine Tool

It were researched that the modeling methods of machine accuracy and the control techniques of the error compensation based on BP neural network(BPNN) for parallel machine tool(PMT)with five degrees of freedom(DOF). The samples are obtained to train the BP neural network which has good capacity for non- liner mapping, learning and generalization. The machine accuracy mathematics model is established for the error compensation, in order to study the nonlinear input and output problem of the parallel machine which difficultly modeling described. The trained neural network was applied to error compensation of PMT to realize modifying errors real-timely. Finally, simulation analysis was performed through the MATLAB software. The results expressed that the control strategies for error compensation were simple, efficient and practicable. Machine accuracy can be increased greatly after compensation.

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.

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