FPGA implementation of a neural network classifier for gas sensor array applications

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 microhotplate 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. ©2009 IEEE.