Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)

作     者：Benrekia, Faycal Attari, Mokhtar Bouhedda, Mounir 

作者机构：Fac Elect & Comp Lab Instrumentat LINS Algiers 16111 Algeria Fac Sci & Technol Dept Elect Engn & Comp Uyfm 26000 Medea Algeria 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2013年第13卷第3期

页      面：2967-2985页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

主　　题：e-nose gas sensor array pattern recognition neural network classifier pic-microcontroller FPGA-implementation 

摘      要：This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.