PLD implementation of neural network for industrial gas discrimination system

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