Electronic nose drift general calibration method based on convex set projection and extreme learning machine
A technology of extreme learning machine and convex set projection, which is applied in scientific instruments, computer components, standard gas analyzers, etc., can solve the problems of domain migration ability and generalization limitations, poor generalization, and gas identification applications, etc., to achieve Good generalization and migration ability, improved gas recognition accuracy, and simple learning process
A technology of extreme learning machine and convex set projection, which is applied in scientific instruments, computer components, standard gas analyzers, etc., can solve the problems of domain migration ability and generalization limitations, poor generalization, and gas identification applications, etc., to achieve Good generalization and migration ability, improved gas recognition accuracy, and simple learning process
CN113804833AActive Publication Date: 2021-12-17CHONGQING UNIV +1
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[0057] The embodiment of the present invention will be explained in detail below in conjunction with the accompanying drawings. The examples given are only for the purpose of illustration, and cannot be interpreted as limiting the present invention. The accompanying drawings are only for reference and description, and do not constitute the scope of patent protection of the present invention. limitations, since many changes may be made in the invention without departing from the spirit and scope of the invention.
[0058] In order to improve the gas recognition accuracy and generalization of the electronic nose after its gas sensor drifts, the embodiment of the present invention provides a general calibration method for electronic nose drift based on convex set projection and extreme learning machine, such as figure 1 , 4 shown, including steps:
[0059] S0. Collect the undrifted and drifted output responses of a sensor array composed of multiple sensors in a variety of standa...
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The invention relates to the technical field of electronic nose calibration, and particularly discloses an electronic nose drift general calibration method based on convex set projection and an extreme learning machine. From the perspective of machine learning, a constraint network net1 and a calibration network net2 are established based on the extreme learning machine, the constraint network net1 is trained by using an electronic nose in a feature data set X without drift, network parameters are stored, then a drifting feature data set Xd is taken as an input of a constraint network net1, iterative adjustment is performed on the input Xd of the net1 network based on convex set projection to obtain a calibrated sensor feature data set Xc, and then the feature data set Xc is taken as a label of a calibration network net2; and the feature data set Xd is input for common training so as to calibrate an unknown gas response signal so that the tolerance performance of gas identification after drifting of the electronic nose can be improved, and a network obtained by training can achieve a drifting compensation effect on an unknown gas sample. Therefore, the gas recognition precision of the electronic nose after other gas sensors drift is improved.
Description
technical field [0001] The invention relates to the technical field of electronic nose calibration, in particular to a general calibration method for electronic nose drift based on convex set projection and extreme learning machine. 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, a...
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Patent Timeline
17 Dec 2021
Publication
CN113804833A
- IPC
- G01N33/00; G06K9/62; G06N3/04
- CPC
- G01N33/0006; G06N3/048; G06F18/214
- Inventors
- 田逢春; 李翰韬