An Improved Algorithm of Drift Compensation for Olfactory Sensors

This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data.

Automated summary

This research focuses on the semi-supervised learning algorithm for different domain data in machine olfaction, aiming to address the issue of sensor drift. A novel algorithm, DTSWKELM, is proposed to transform the data through domain transformation and solve the semi-supervised classification problem of different domain data as a problem of the same domain data.