A spiking neural network for gas discrimination using a tin oxide sensor array

被引：8

作者：

Ambard, Maxime ^{[1
]}

Guo, Bin ^{[2
]}

Martinez, Dominique ^{[1
]}

Bermak, Amine ^{[2
]}

机构：

[1] LORIA INRIA, CORTEX Grp, Nancy, France

[2] Hong Kong Univ Sci & Technol, Elect Comp Engn Dept, Hong Kong, Hong Kong, Peoples R China

来源：

DELTA 2008: FOURTH IEEE INTERNATIONAL SYMPOSIUM ON ELECTRONIC DESIGN, TEST AND APPLICATIONS, PROCEEDINGS | 2008年

关键词：

tin oxide; gas sensor array; spike timing computation; supervised learning;

D O I：

10.1109/DELTA.2008.116

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a bio-inspired signal processing method for odor discrimination. A spiking neural network is trained with a supervised learning rule so as to classify the analog outputs from a monolithic 4x4 tin oxide gas sensor array implemented in our in-house 5 mu m process. This scheme has been sucessfully tested on a discrimination task between 4 gases (hydrogen, ethanol, carbon monoxide, methane). Performance compares favorably to the one obtained with a common statistical classifier. Moreover, the simplicity of our method makes it well suited for building dedicated hardware for processing data from gas sensor arrays.

引用

页码：394 / +

页数：2

共 12 条

[1] Cross-reactive chemical sensor arrays [J].