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

Electronic nose gas identification method based on source domain transfer limit learning drift compensation

An extreme learning and drift compensation technology, applied in scientific instruments, instruments, measuring devices, etc., can solve problems such as improving the gas recognition accuracy of electronic noses, difficulty in drift compensation, and the inability of the recognition neural network to correctly identify matching gases.

Active Publication Date: 2017-08-25
CHONGQING UNIV
View PDF6 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] A typical multivariate component correction method is the component correction principal component analysis method, which uses principal component analysis to find the drift direction, thereby removing the drift component; however, the compensation idea of ​​the component correction principal component analysis method needs to be based on the drift of all categories of data However, the actual drift of the electronic nose is not the case, so it is difficult to effectively apply this method to the drift compensation of the electronic nose; and if a multiplier correction is added on the basis of the component correction principal component analysis method Variables are used to improve the consistency limitation of the data drift direction, and the generalization of its drift compensation will be restricted by the nonlinear dynamic characteristics of the gas sensor in online applications, making it difficult for its drift compensation effect to be specific to different Suitable for a wide range of gas identification applications
[0007] The adjustment compensation method is to adjust the difference in the distribution of the sensing features by adjusting the response changes of the gas sensor array of the electronic nose during gas recognition and detection at different stages, thereby realizing drift compensation; When the gas sensor array of the electronic nose has a transient response, it is misjudged that the gas sensor array is undergoing drastic changes in drift, and then frequently adjusted and compensated, it is easy to disrupt the original eigenvalue distribution of the gas sensor array of the electronic nose. As a result, the original relatively accurate identification neural network cannot correctly identify its matching gas after drift compensation, which affects the gas identification accuracy of the electronic nose
[0008] Previously, researchers have also carried out some research on the drift compensation of electronic noses through machine learning methods, but the machine learning methods currently used are mainly based on support vector machines, which often require a large number of training samples for learning. In the case of limited training samples, the compensation effect is not good, and the gas recognition accuracy of the electronic nose cannot be improved through drift compensation. In addition, this type of machine learning method usually needs to train many base classifiers, so its domain migration ability and generalization sex is restricted
[0009] To sum up, in the existing technology, the compensation method for the drift of the electronic nose gas sensor generally has the problems of low gas recognition accuracy, poor migration ability and generalization of the electronic nose after compensation.

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
  • Electronic nose gas identification method based on source domain transfer limit learning drift compensation
  • Electronic nose gas identification method based on source domain transfer limit learning drift compensation
  • Electronic nose gas identification method based on source domain transfer limit learning drift compensation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0068] Aiming at the problem that the gas sensor drift of the electronic nose affects the gas identification accuracy, the present invention provides an electronic nose gas identification method based on source domain migration limit learning drift compensation, and analyzes and solves the problem from the perspective of a machine learning machine , a concept based on source domain migration limit learning is proposed, and the source domain data set and the target domain data set are respectively constructed with the help of a small number of electronic noses with the tagged gas sensor array sensing data matrix collected without drifting and drifting , used for source domain migration limit learning to obtain a robust identification classifier, which can improve the tolerance performance of the identification classifier for gas identification after the electronic nose drifts, and then use the identification classifier obtained after learning to perform When identifying the gas ...

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 provides an electronic nose gas identification method based on source domain migration extreme learning to realize drift compensation. According to the source domain migration extreme learning to realize drift compensation, a source domain migration extreme learning machine framework is proposed from the perspective of machine learning and used for solving the problem of sensor drift instead of direct correction for single sensor response; a source domain data set and a target domain data set are built according to labeled gas sensor array sense data matrixes collected by an electronic nose before drift and after drift respectively and are taken as inputs of an extreme learning machine for training an identification classifier of the electronic nose, so that the tolerance performance of the identification classifier on gas identification after the electronic nose drifts is improved, and the purposes of drift compensation and gas identification precision improvement are achieved; besides, technical advantages of the extreme learning machine are kept, and accordingly, the method has better generalization performance and migration performance. Therefore, based on the source domain migration extreme learning machine framework provided by the invention, one learning framework with good learning capacity and generalization capacity is built.

Description

technical field [0001] The invention relates to the technical field of electronic nose detection, in particular to an electronic nose gas identification method based on source domain migration limit learning drift compensation. Background technique [0002] An electronic nose is an intelligent electronic device or artificial olfactory system that uses the response map of a gas sensor array to identify gases. Due to the crossover characteristics and broad spectrum of gas sensor arrays in electronic noses, the gas recognition capabilities of electronic noses are widely used in medical diagnosis, tea quality assessment, environmental detection, and gas concentration prediction. [0003] However, the gas sensor of the electronic nose is aging continuously with the increase of usage time, which greatly shortens the service life of the gas sensor array of the electronic nose. Poisoning, aging or environmental variables can cause the gas sensor drift of the electronic nose, and th...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G01N33/00
CPCG01N33/0062G01N2033/0068
Inventor 张磊刘燕邓平聆田逢春
Owner CHONGQING UNIV
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