支持向量机
极限学习机
电子鼻
模式识别(心理学)
特征提取
主成分分析
人工智能
径向基函数
瞬态(计算机编程)
核(代数)
生物系统
计算机科学
数学
人工神经网络
生物
操作系统
组合数学
作者
Rabeb Faleh,Sami Gomri,Khalifa Aguir,Abdennaceur Kachouri
出处
期刊:Sensor Review
[Emerald (MCB UP)]
日期:2021-10-01
卷期号:41 (5): 437-448
被引量:4
标识
DOI:10.1108/sr-02-2021-0066
摘要
Purpose The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.
科研通智能强力驱动
Strongly Powered by AbleSci AI