NDLI: PLD implementation of neural network for industrial gas discrimination system

Content Provider	IEEE Xplore Digital Library
Author	U-Trum Jang Wan-Young Chung
Copyright Year	2001
Description	Author affiliation: Dept. of Internet Eng., Dongseo Univ., Pusan, South Korea (U-Trum Jang)
Abstract	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 4 metal oxide gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision part in PLD (programmable logic device) chip. The sensor array consists of four commercial, tin oxide based, semiconductor type gas sensors. BP (back propagation) neural networks with multilayer perceptron structure was designed and implemented on CPLD of fifty thousand gate level chip by VHDL language for processing the input signals from 4 gas sensors and qualification of industrial gases in air. The network contained four input units, one hidden layer with 4 neurons and output with 4 regular neurons. The 'electronic nose' system successfully discriminated between 4 kinds of industrial gases in computer simulation.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	454
Ending Page	458
File Size	369815
Page Count	5
File Format	PDF
ISBN	0780370902
DOI	10.1109/ISIE.2001.931833
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-06-12
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Gas industry Sensor arrays Gas detectors Electronic noses Programmable logic arrays Gases Neurons Pattern recognition Programmable logic devices
Content Type	Text
Resource Type	Article

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