Title :
Mixed Gases Recognition Based on Feedforward Neural Network
Author :
Tao, Zhou ; Lei, Wang
Author_Institution :
Coll. of Electron. & Inf. Eng., TongJi Univ., Shanghai
Abstract :
The three gas sensors array was developed in this paper, and it was encapsulated through the Micro-Electro-Mechanical Systems (MEMS) technique. The gas sensors applied the heating unit to improve the sensitivity. The gas sensor which was sensitive to the special gas could be selected in the different application fields. The sampling experiments showed that the gas sensors have the higher sensitivity and better repeatability and cross sensitivity. Moreover, the pattern recognition algorithms based on a feedforward neural network were studied in the paper. They have the higher pattern recognition capacity, the convergence rate and simpler training method. The intelligent recognition system which adopted the gas sensor array and feedforward neural network was design. The recognition experiments showed the system has better identification effect and higher accuracy.
Keywords :
computerised instrumentation; feedforward neural nets; gas sensors; microsensors; pattern recognition; sensor arrays; feedforward neural network; gas sensors array; intelligent recognition system; microelectromechanical systems technique; mixed gases recognition; pattern recognition algorithms; Feedforward neural networks; Gas detectors; Gases; Heating; Microelectromechanical systems; Micromechanical devices; Neural networks; Pattern recognition; Sampling methods; Sensor arrays; algorithm; feedforward neural network; gas sensors array; mixed gases; pattern recognition;
Conference_Titel :
Intelligent Information Technology and Security Informatics, 2009. IITSI '09. Second International Symposium on
Conference_Location :
Moscow
Print_ISBN :
978-1-4244-3580-7
DOI :
10.1109/IITSI.2009.35