Gas identification with tin oxide sensor array and self-organizing maps: Adaptive correction of sensor drifts

被引：75

作者：

Marco, S ^{[1
]}

Ortega, A ^{[1
]}

Pardo, A ^{[1
]}

Samitier, J ^{[1
]}

机构：

[1] Univ Barcelona, Dept Elect, Barcelona, Spain

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 1998年 / 47卷 / 01期

关键词：

data fusion; drift; gas sensors; neural networks; pattern recognition; SOM; tin oxide;

D O I：

10.1109/19.728841

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Low-cost tin oxide gas sensors are inherently nonspecific, In addition, they have several undesirable characteristics such as slow response, nonlinearities, and long-term drifts, This paper shows that the combination of a gas-sensor array together with self-organizing maps (SOM's) permit success in gas classification problems, The system is able to determine the gas present in an atmosphere with error rates lower than 3%. Correction of the sensor's drift with an adaptive SOM has also been investigated.

引用

页码：316 / 321

页数：6

共 17 条

[1] NEURAL NETWORK APPROACHES VERSUS STATISTICAL-METHODS IN CLASSIFICATION OF MULTISOURCE REMOTE-SENSING DATA [J].