Study on soft-sensing model for condenser vacuum based-on Support Vector Regression

2010 ◽  
Author(s):  
Lei Wang ◽  
Rui-qing Zhang
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Soft Sensing ◽  
Sensing Model

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 Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm

2009 ◽  
Vol 15 (3) ◽  
pp. 175-187 ◽  
Author(s):  
S.K. Lahiri ◽  
Nadeem Khalfe
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Support Vector ◽  
Petrochemical Plant ◽  
Process Data ◽  
Soft Sensing ◽  
Modeling And Optimization ◽  
Sensor Modeling

Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR), a new powerful machine learning method based on a statistical learning theory (SLT) into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression - genetic algorithm (SVR-GA) for modeling and optimization of mono ethylene glycol (MEG) quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc.) is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.

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