scholarly journals Combined soft measurement on key indicator parameters of new competitive advantages for China's export

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taosheng Wang ◽  
Hongyan Zuo ◽  
C. H. Wu ◽  
B. Hu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Measurement Model ◽  
Evaluation Index System ◽  
Support Vector ◽  
Competitive Advantages ◽  
Entropy Weight ◽  
Soft Sensing ◽  
Soft Measurement ◽  
Sensing Model

AbstractThe estimation of the difference between the new competitive advantages of China's export and the world’s trading powers have been the key measurement problems in China-related studies. In this work, a comprehensive evaluation index system for new export competitive advantages is developed, a soft-sensing model for China’s new export competitive advantages based on the fuzzy entropy weight analytic hierarchy process is established, and the soft-sensing values of key indexes are derived. The obtained evaluation values of the main measurement index are used as the input variable of the fuzzy least squares support vector machine, and a soft-sensing model of the key index parameters of the new export competitive advantages of China based on the combined soft-sensing model of the fuzzy least squares support vector machine is established. The soft-sensing results of the new export competitive advantage index of China show that the soft measurement model developed herein is of high precision compared with other models, and the technical and brand competitiveness indicators of export products have more significant contributions to the new competitive advantages of China's export, while the service competitiveness indicator of export products has the least contribution to new competitive advantages of China's export.

Soft-Sensing Model of Flatness Error on the Surface of Machining Workpiece and its Application

2014 ◽  
Vol 628 ◽  
pp. 436-441
Author(s):  
Ji Ping Lei ◽  
Jian Mei Chen
Keyword(s):  
Relative Error ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Soft Sensing ◽  
Feed Drive ◽  
Soft Measurement ◽  
Sensing Model ◽  
Allowable Error ◽  
Flatness Error ◽  
Moving Speed

To effectively realize fast and high accurate measurements of flatness error on the surface of machining workpiece, multiple sets of actual machining experimental data are used as samples, a soft-sensing model of flatness error on the surface of machining workpiece is established by using the speed n, the moving speed of carriage uy and the voltage U of piezoelectric ceramic micro-feed drive as arguments with SVM(Support Vector Machine), and adaptive genetic algorithm is used to optimize the allowable error ε, the number of positive gasification rules c and the parameters of kernel function r, the results of training, testing and practical application show, after the optimization of 200 steps, training mean relative error which became saturated is 3.4%, testing relative error is less than 2.6%, the range of average relative error between the soft measurement value of flatness error on the surface of machining workpiece and the test value of L-730 laser flatness measuring instrument is 1.2% to 2.4%.

Research and Development of Sucrose Supersaturation Soft Measurement System Based on Least Square Support Vector Machine

2013 ◽  
Vol 694-697 ◽  
pp. 1229-1232
Author(s):  
Yan Mei Meng ◽  
Guan Cheng Lu ◽  
Quan Zhou ◽  
Chun Wa Qin ◽  
Hai Feng Pang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Measurement Model ◽  
Least Square ◽  
Support Vector ◽  
Auxiliary Variables ◽  
Noise Interference ◽  
Kernel Partial Least Squares ◽  
Soft Measurement ◽  
Measurement Principle

Nowadays, it's difficult to stably measure sucrose supersaturation through an online instrument directly. This paper is Nowadays, it's difficult to stably measure sucrose supersaturation through an online instrument directly. This paper is based on the soft measurement principle，extracting the principal component of auxiliary variables in sucrose supersaturation soft measuring with the method of kernel partial least squares, and eliminating the multiple nonlinear correlation among the auxiliary variables and noise interference, building an online soft measurement of sucrose supersaturation and offline soft measurement model，and improving the accuracy of soft measurement . The paper develops the system of sucrose supersaturation soft measurement by using VC + +6.0.

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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