Study on electronic-nose recognition method with independent component analysis and back-propagation neural network

WANG Yan~1; CHEN Xiang-dong~1; JIANG Ya-dong~2; ZHAO Jing~1

doi:10.13873/j.1000-97872006.09.012

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Study on electronic-nose recognition method with independent component analysis and back-propagation neural network

更新时间：2025-09-11

- Study on electronic-nose recognition method with independent component analysis and back-propagation neural network
- Issue 9, Pages: 36-39(2006)
- 作者机构：
  
  1. 西南交通大学信息科学与技术学院
  2. 电子科技大学光电信息学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872006.09.012
  CLC： TP212;TP391.4
- Published：2006
- 稿件说明：
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[1]王岩,陈向东,蒋亚东,赵静.基于独立分量分析和BP网络的电子鼻模式识别[J].传感器与微系统,2006(09):36-39.

WANG Yan~1, CHEN Xiang-dong~1, JIANG Ya-dong~2, et al. Study on electronic-nose recognition method with independent component analysis and back-propagation neural network[J]. 2006, (9): 36-39.
[1]王岩,陈向东,蒋亚东,赵静.基于独立分量分析和BP网络的电子鼻模式识别[J].传感器与微系统,2006(09):36-39. DOI： 10.13873/j.1000-97872006.09.012.

WANG Yan~1, CHEN Xiang-dong~1, JIANG Ya-dong~2, et al. Study on electronic-nose recognition method with independent component analysis and back-propagation neural network[J]. 2006, (9): 36-39. DOI： 10.13873/j.1000-97872006.09.012.

摘要

为了提高电子鼻对混合气体的识别率

针对气体传感器阵列的交叉敏感特性

探讨了在电子鼻系统中基于独立分量分析（ICA）算法与BP神经网络相结合进行模式识别的可行性。并对4个气体传感器组成的电子鼻对4种气体混合物所测得的原始数据进行处理

结果表明:ICA算法对数据进行有效预分类

减少了样本之间的相关性

将生成的新样本作为BP网络的输入

使网络结构简化

在保证一定正确率的前提下

大大提高网络的学习速度。利用该方法可以提高电子鼻识别混合气体的准确率。

Abstract

Independent component analysis（ICA） and back propagation（BP） neural network are used for pattern recognition of Electronic-nose.Considered the cross sensitivity of gas sensor array for improving the identify ratio to electronic-nose.The by using a sensor array of four sensors is analyzed to measure data which is obtained mixture of CO

and C

.The results showed that ICA can give a good classification for the gases data and eliminate the data correlation.The processing data is inputed to BP network

network structure is simplified

and the convergence speed of the BP network is enhanced greatly.Further the recognition ratio is improved highly with ICA and BP network to electronic-nose system.

关键词

Keywords

references

径向基函数网络预测混合气体浓度 [J]. 孙广清,王磊. 传感技术学报 . 2004(02)

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