传感技术学报2016，Vol.29Issue(7)：1109-1114,6.DOI:10.3969/j.issn.1004-1699.2016.07.026
基于遗传算法的小波神经网络在多组分气体检测中的应用
Application of Wavelet Neural Network Based on Genetic Algorithm in Multi Component Gas Detection
刘文贞 1陈红岩 1袁月峰 1郭晶晶 1李孝禄1
作者信息
- 1. 中国计量学院机电工程学院，杭州310018
- 折叠
摘要
Abstract
Because of the simultaneous measurement of automobile exhaust gas by using the multi-component gas sensor,the gas concentration is the result of the cross absorption and interference,resulting in the large measure⁃ment error and low accuracy. Aiming at this problem,the genetic algorithm optimization of the wavelet neural net⁃work is used to establish the three component gas quantitative analysis model based on infrared spectrum. The con⁃centration signals of CO2,CO,HC,as the model input,through the model regression analysis,to get the correspond⁃ing mixed gas concentration and solve the problem of mutual interference. Finally,the model performance is simu⁃lated by the experimental data. The results show that the average error of the model is significantly reduced com⁃pared to the traditional model.关键词
汽车尾气/交叉吸收干扰/小波神经网络/遗传算法Key words
automobile tail gas/cross absorption interference/wavelet neural network/genetic algorithm分类
计算机与自动化引用本文复制引用
刘文贞,陈红岩,袁月峰,郭晶晶,李孝禄..基于遗传算法的小波神经网络在多组分气体检测中的应用[J].传感技术学报,2016,29(7):1109-1114,6.
