An Improved Algorithm of Drift Compensation for Olfactory Sensors
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Chemical Sensor Technologies · Machine Learning and ELM · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Funding
Sichuan Science and Technology Program
References (31)
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