A Nonlinear Manifold Embedding Extreme Learning Machine to Improve the Gas Recognition of Electronic Noses-Reference-Cited by-同舟云学术

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A Nonlinear Manifold Embedding Extreme Learning Machine to Improve the Gas Recognition of Electronic Noses

Published:2024-05-01 Issue:9 Volume:24 Page:15046-15054
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Tian Yutong¹^ORCID,Liu Tao²^ORCID,Li Tingjun¹,Yang Haining¹^ORCID,Yan Jia³^ORCID

Affiliation:

1. School of Electronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China

2. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

3. College of Artificial Intelligence, Southwest University, Chongqing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing, China

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10516472/10473709.pdf?arnumber=10473709

Reference39 articles.

1. Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

2. Non-Invasive Method for Tuberculosis Exhaled Breath Classification Using Electronic Nose

3. A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases

4. Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

5. Principles and recent advances in electronic nose for quality inspection of agricultural and food products

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