摘要
This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural network(RBFNN).The GA is used to screen the infrared spectrum of the mixed gas, while the selected spectral region is used as the input of the RBFNN to establish a calibration model to quantitatively analyze the components of logging gas. The analysis results demonstrate that the proposed GA-RBFNN performs better than FS-RBFNN and ES-RBFNN, and our proposed method is feasible.
作者
SONG Li-mei
GUO Su-qing
YANG Yan-gang
GUO Qing-hua
WANG Hong-yi
XIONG Hui
宋丽梅;郭素青;杨燕罡;郭庆华;王红一;熊慧(Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China;School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China;School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong NSW2500, Australia)
基金
supported by the Natural Science Foundation of Tianjin(Nos.16JCQNJC02100,15JCYBJC51700 and 16JCYBJC15400)
the National Key Scientific Instrument and Equipment Development Project of China(No.2012YQ0901670602)
the State Key Laboratory of Precision Measuring Technology and Instruments(Tianjin University,No.PIL1603)
the Program for Innovative Research Team in University of Tianjin(No.TD13-5036)
