Soft-Sensing Model of Deformation of Welded Steel Structure Based on FLS-SVM and its Application

2014 ◽  
Vol 628 ◽  
pp. 152-156
Author(s):  
Ji Ping Lei ◽  
Jian Mei Chen
Keyword(s):  
Relative Error ◽  
Welding Current ◽  
Steel Structure ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Practical Application ◽  
Soft Sensing ◽  
Welded Steel ◽  
Welded Structure ◽  
Sensing Model

To effectively achieve rapid and high-precision measurements of the deformation of steel welded structure, multiple sets of the actual experimental data of steel welded structure are used as the samples, the soft-sensing model of deformation of welded steel structure, which uses the welding current I, the welding voltage U, the welding speed v and the flow of gas qm as arguments, is established by fuzzy least squares support vector machine, and adaptive genetic algorithm is used to optimize the number of positive gasification rules c and the parameters of kernel function σ, training, testing and practical application results show, the optimization of 200 steps, the training relative error which become saturated is 2.43%, the testing relative error is less than 2.45%.

Soft-Sensing Model on the Roughness of Machining Surface under the Numerical Control and its Application

2011 ◽  
Vol 48-49 ◽  
pp. 1077-1085 ◽  
Author(s):  
Yi Hui Zeng ◽  
Jia Qiang E ◽  
Xian Ping Yang ◽  
Hong Mei Li
Keyword(s):  
Relative Error ◽  
Numerical Control ◽  
Average Relative Error ◽  
Function Parameter ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Soft Sensing ◽  
Vector Machines ◽  
Sensing Model ◽  
Allowable Error

In order to make sure a high-accuracy and fast- speed survey, a Soft-sensing model for the roughness of machining surface was built based on the support vector machines using rotate speed n, feed peed vf, and depth of cutting as independent parameters, taking groups of actual machining experiment data as samples.The allowable error ε and the positive aligned c and the kernel function parameter r were optimized by an adaptive genetic algorithm. After being optimized 300 steps, the following results can be gained through the training, testing and application. The average relative error tended to saturation training was 4.0%; the test error was less than 2.6%; the average relative error between the Soft-sensing value for the roughness of machining surface under the numerical control and the test value of the profile and roughness tester for the SV-C3000 super surface of was ranging from 0.4% to 1.25%.

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%.

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.

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