Title :
Knowledge-based genetic algorithms data fusion and its application in mine mixed-gas detection
Author :
Zhang, Qian ; Li, Haigang ; Tang, Zhongyu
Author_Institution :
Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China
Abstract :
Considering that the high concentration of mine gas and hydrogen will disturb the output of electrochemical carbon monoxide sensor, this paper integrates gas sensor array with data fusion Algorithm. The output signals of three sensors are trained by BP neural network to get the mathematical model of information fusion for the analysis of mixed gas of methane, hydrogen and carbon monoxide. The experiment shows that the information fusion could correct the crossed sensitivity error, and improve the accuracy of carbon monoxide, therefore achieve quantitative analysis mixed gas of coal mine.
Keywords :
backpropagation; electrochemical sensors; gas sensors; genetic algorithms; knowledge based systems; mining industry; neural nets; sensor fusion; BP neural network; data fusion; electrochemical carbon monoxide sensor; gas sensor array; hydrogen; information fusion; knowledge-based genetic algorithms; mathematical model; mine gas; mine mixed-gas detection; Error correction; Gas detectors; Genetic algorithms; Hydrogen; Information analysis; Mathematical model; Neural networks; Sensor arrays; Sensor fusion; Signal analysis; Gas Sensor; Genetic Algorithm; Information Fusion; Neural Networks;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5498184
