Electronic nose heterogeneous data recognition method based on target domain transfer extreme learning

A recognition method, heterogeneous data technology, applied in the direction of neural learning methods, character and pattern recognition, scientific instruments, etc., can solve the difficulty of drift compensation, domain migration ability and generalization limitations, disrupt the electronic nose gas sensor array Eigenvalue distribution law and other issues

A recognition method, heterogeneous data technology, applied in the direction of neural learning methods, character and pattern recognition, scientific instruments, etc., can solve the difficulty of drift compensation, domain migration ability and generalization limitations, disrupt the electronic nose gas sensor array Eigenvalue distribution law and other issues

CN105913079BActive Publication Date: 2019-04-23CHONGQING UNIV
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  • Electronic nose heterogeneous data recognition method based on target domain transfer extreme learning
  • Electronic nose heterogeneous data recognition method based on target domain transfer extreme learning
  • Electronic nose heterogeneous data recognition method based on target domain transfer extreme learning

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Embodiment Construction

[0079] Aiming at the problem that the drift of the gas sensor of the electronic nose affects the accuracy of gas recognition, the present invention provides an electronic nose heterogeneous data recognition method based on target domain migration limit learning, and analyzes and solves the problem from the perspective of a machine learning machine , a concept based on target domain migration limit learning is proposed, with the help of a small number of electronic noses that collect the gas sensor array sensing data matrix when there is no drift and the labeled and unlabeled gas sensor array sensing data collected after drift Data matrix, construct source domain data set, target domain data set and test domain data set respectively, to carry out extreme learning of target domain domain migration to obtain a robust recognition classifier, which can improve the recognition classifier in electronic nose Tolerance performance of gas recognition after drifting, when the recognition ...

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Abstract

The invention provides an electronic nasal heterogeneous data recognition method based on target domain migration extreme learning. From the perspective of machine learning, it proposes the domain migration extreme learning machine framework to solve the problem of sensor drift.Gas sensor matrix sensor sensor matrix and label and label -free gas sensor arrays collected after drift are constructed from source domain data sets, target domain data sets, and data sets to be tested, respectively.The input of the machine is learned to the electronic nose recognition classifier to improve the tolerance of the identification classifier after drift of the electronic nose.The purpose, with the technical advantages of extreme learning machines, enables this method to have better generalization and migration performance, which can be widely used in different electronic nose products for different gas recognition applications.

Description

technical field [0001] The invention relates to the technical field of electronic nose detection, in particular to an electronic nose heterogeneous data identification method based on target domain migration limit learning. 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 the ga...

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Application Information

Patent Timeline
23 Apr 2019
Publication
CN105913079B
IPC
G06K9/62; G06N3/08; G01N27/00
CPC
G06N3/08; G01N27/00; G06F18/2413
Inventors
张磊; 邓平聆