FPGA IMPLEMENTATION OF A NEURAL NETWORK CLASSIFIER FOR GAS SENSOR ARRAY APPLICATIONS

被引：0

作者：

Benrekia, Faycal ^{[1
,2
]}

Attari, Mokhtar ^{[2
]}

Bermak, Amine ^{[3
]}

Belhout, Khaled ^{[1
]}

机构：

[1] UYFM Medea, Dept Elect Engn, Ain Dheb 26000, Medea, Algeria

[2] USTHB, LINS Lab Instrumentat, Fac Elect & Comp, Algiers 16111, Algeria

[3] Hong Kong Univ Sci & Technol, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China

来源：

2009 6TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS AND DEVICES, VOLS 1 AND 2 | 2009年

关键词：

E-nose; Gas sensor; Neural network classifier; VHDL; FPGA-implementation;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A primitive gas recognition system which can discriminate limited species of industrial gas was designed and simulated. The 'electronic nose' consists of an array of 8 micro-hotplate based SnO2 thin film gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision part in programmable logic device chip. BP (Back Propagation) neural networks with Multilayer Perceptron structure was designed and implemented on FPGA (Field Programmable Gate Array), of twenty thousand gate level chip by VHDL language for processing the input signals from 8 kinds of gas sensors. The network contained eight input units, one hidden layer with 4 neurons and output with 5 regular neurons. The 'electronic nose' system successfully discriminated 5 kinds of industrial gases in computer simulation. A small application has been tested on the APS X208 FPGA test board.

引用

页码：1040 / 1045

页数：6

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