Novel temperature modeling and compensation method for bias of ring laser gyroscope based on least-squares support vector machine

2011 ◽  
Vol 9 (5) ◽  
pp. 051201-51204 ◽  
Author(s):  
Xudong Yu ◽  
Yu Wang ◽  
Guo Wei ◽  
Pengfei Zhang ◽  
Xingwu Long
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Ring Laser ◽  
Compensation Method ◽  
Support Vector ◽  
Temperature Modeling ◽  
Ring Laser Gyroscope ◽  
Laser Gyroscope

scholarly journals Temperature compensation method using readout signals of ring laser gyroscope

2015 ◽  
Vol 23 (10) ◽  
pp. 13320 ◽  
Author(s):  
Geng Li ◽  
Fei Wang ◽  
Guangzong Xiao ◽  
Guo Wei ◽  
Pengfei Zhang ◽  
...  
Keyword(s):  
Ring Laser ◽  
Temperature Compensation ◽  
Compensation Method ◽  
Ring Laser Gyroscope ◽  
Laser Gyroscope

Temperature Compensation of RLG’s Bias Based on LS-SVM

2011 ◽  
Vol 298 ◽  
pp. 1-6
Author(s):  
Guo Wei ◽  
Peng Fei Zhang ◽  
Xun Jin ◽  
Xing Wu Long
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Regression Model ◽  
Least Squares ◽  
Ring Laser ◽  
Temperature Compensation ◽  
Maximum Error ◽  
Support Vector ◽  
Least Squares Regression ◽  
Dynamic Temperature

. Bias of ring laser gyro (RLG) changes with temperature in the non-linear way, which is an important restraining factor for improving the accuracy of RLG. For the deficiency of least squares regression and neural networks, a new method of temperature compensation of RLG’s bias was proposed, that is, building function regression model by using Least Squares-Support Vector Machine(LS-SVM). Static and dynamic temperature experiments of RLG’s bias are carried out. The results show that: after static temperature compensation, the maximum error of RLG’s bias has dropped from 0.0413º/hr to 0.00073º/hr; while after dynamic temperature compensation, the gyro precision has increased from = 0.0102º/hr to = 0.0011º/hr. It indicates that this method has improved the laser gyro’s accuracy considerably.

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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