.png){kind=icon}
返回
Pattern Analysis for Machine Olfaction: A Review
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2002.800688.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute of the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics has been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.
Keyword:
Classification
clustering
dimensionality reduction
electronic nose
multicomponent analysis
pattern analysis
preprocessing
validation
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏Laser-Induced Breakdown Spectroscopy (LIBS) Analysis of Calcium Ions Dissolved in Water Using Filter Paper Substrates: An Ideal Internal Standard for Precision Improvement使用滤纸基底对溶解在水中的钙离子进行激光诱导击穿光谱 (LIBS) 分析: 提高精度的理想内标
PREAI
分享收藏Real-time and offline techniques for identifying obstructive sleep apnea patients用于识别阻塞性睡眠呼吸暂停患者的实时和离线技术
PREAI
分享收藏
查看更多内容