Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

RReliefF variable selection based production process primary variable streamline soft measuring method

A technology that dominates the variable and production process, applied in the field of soft measurement, can solve the problems of huge calculation amount, time-consuming and labor-intensive, and achieve the effect of saving human, material and financial resources and improving the efficiency of measurement.

Inactive Publication Date: 2013-04-10
西南兵工重庆环境保护研究所有限公司
View PDF2 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Computing many auxiliary variables to realize the soft measurement of the leading variable will bring a huge amount of calculation, which is not only time-consuming and labor-intensive, but also the obtained soft measurement results are not necessarily the best, which is in the generation process unwanted things

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • RReliefF variable selection based production process primary variable streamline soft measuring method
  • RReliefF variable selection based production process primary variable streamline soft measuring method
  • RReliefF variable selection based production process primary variable streamline soft measuring method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] Such as figure 1 , a production process leading variable simplification and soft-sensing method based on RReliefF variable selection is carried out as follows:

[0048] Step 1: Determine n original auxiliary variables that may be related to the leading variable, collect the values ​​​​of n original auxiliary variables and leading variables, and form a sample set. The size of the sample set is m, and the sample size is n+1;

[0049] Each sample of the sample contains values ​​of a total of n+1 variables including one dominant variable and n original auxiliary variables.

[0050] Step 2: Use the RReliefF algorithm to calculate the weight values ​​of n original auxiliary variables;

[0051] Using the RReliefF algorithm to calculate the weight value of any original auxiliary variable A among the n original auxiliary variables is carried out as follows:

[0052] (1) Select a sample D from the sample set i , select the k samples closest to the sample Di from the remaining ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an RReliefF variable selection based production process primary variable soft measuring method. The RReliefF variable selection based production process primary variable soft measuring method comprises the following steps: firstly, n original auxiliary variables possibly related to a primary variable are confirmed, and assigned data of the n original auxiliary variables and the primary variable are acquired to form a sample set; secondly, the weighted values of the n original auxiliary variables are respectively calculated with the RReliefF algorithm; thirdly, an original auxiliary variable sequence is formed; fourthly, modeling is performed, and an optimal auxiliary variable is determined based on a minimum mean square error (MSE); and fifthly, a streamline soft-measuring model is obtained. According to the RReliefF variable selection based production process primary variable soft measuring method, the primary variable can be modeled with an auxiliary variable set containing the least number of auxiliary variables found on best modeling effect basis, and the soft measuring of the primary variable streamline is achieved.

Description

technical field [0001] The invention belongs to the technical field of soft measurement, and in particular relates to a soft measurement method for streamlining and simplifying leading variables in a generation process based on RReliefF variable selection. Background technique [0002] So far, in the actual production process, there are many variables that cannot be directly measured due to technical or economic reasons. In this case, soft sensor technology emerges as the times require. Soft sensing is based on the mathematical relationship between measurable and easy-to-measure process variables (called auxiliary variables) and difficult-to-detect directly measured variables (called leading variables), according to a certain optimal criterion, using various calculation methods, using The software realizes the measurement or estimation of the measured variable. Soft sensor technology is a hot spot in current research. For example, Chinese patent (patent number: 200410017533...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G01D21/00
Inventor 李太福颜克胜苏盈盈姚立忠胡文金王美丹
Owner 西南兵工重庆环境保护研究所有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com