Application of feature selection in sensor array optimization of E-nose

ZHAN Qiong; ZHANG Shun-ping; FAN Chao-qun; LI Hua-yao; XIE Chang-sheng

doi:10.13873/j.1000-97872008.02.018

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Application of feature selection in sensor array optimization of E-nose

更新时间：2025-09-11

- Application of feature selection in sensor array optimization of E-nose
- Issue 2, Pages: 114-117(2008)
- 作者机构：
  
  1. 华中科技大学模具技术国家重点实验室纳米材料与智能传感实验室
  2. 华中科技大学模具技术国家重点实验室纳米材料与智能传感实验室,湖北,武汉,430074
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872008.02.018
  CLC： TP21
- Published：2008
- 稿件说明：
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[1]占琼,张顺平,范超群,李华曜,谢长生.特征选择在电子鼻系统阵列优化中的应用[J].传感器与微系统,2008(02):114-117.

ZHAN Qiong, ZHANG Shun-ping, FAN Chao-qun, et al. Application of feature selection in sensor array optimization of E-nose[J]. 2008, (2): 114-117.
[1]占琼,张顺平,范超群,李华曜,谢长生.特征选择在电子鼻系统阵列优化中的应用[J].传感器与微系统,2008(02):114-117. DOI： 10.13873/j.1000-97872008.02.018.

ZHAN Qiong, ZHANG Shun-ping, FAN Chao-qun, et al. Application of feature selection in sensor array optimization of E-nose[J]. 2008, (2): 114-117. DOI： 10.13873/j.1000-97872008.02.018.

摘要

阵列优化是优化电子鼻系统性能的重要方法之一

其通过特征选择来确定阵列中合适的传感器数目和种类

进而优化传感器阵列。特征选择一般有搜索性和非搜索性两类方法。实验采用广义顺序前进算法、标准遗传算法、模拟退火算法和随机搜索算法4种搜索性特征选择算法

实现了可燃性液体检测实验中的阵列优化。比较了这4种算法的搜索策略

同时

还对搜索性特征选择算法中常用的5种类别可分性准则进行了比较。结果表明:广义顺序前进算法在本实验条件下具有较优的搜索效率;而基于类内类间距离的准则J3更能准确反映特征集的类别可分离性。

Abstract

Sensor array optimization is one of the methods to enhance the performance of electronic noses

which uses the feature selection to decide the right kinds and number of gas sensors.Generally there are two different methods for feature selection

searching method and non-searching method.Four searching strategies are adopted in a flammable liquids detection experiment

which are genetic algorithm（GA）

simulated-annealing algorithm

generalized sequential forward selection and random searching algorithm

and their performances are compared.Five kinds of class separative criterion are compared here as well.Results show that generalized sequential forward selection possesses excellent optimization property and spended the least time in this experiment

thus having higher efficiency than other three methods.And the criterion J3 based on class distance can estimate the class separability relatively appropriately.

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references

基于支持向量机的机械故障特征选择方法研究 [J]. 王新峰,邱静,刘冠军. 机械科学与技术 . 2005(09)

嗅觉模拟技术[M]. 化学工业出版社 , 殷勇著, 2005

统计模式识别[M]. 电子工业出版社 , (英)AndrewR.Webb著, 2004

遗传算法及其应用[M]. 人民邮电出版社 , 陈国良等编著, 1996

Optimisation of electronic nose measurements. Part I: Methodology of output feature selection [J] . Sylvie Roussel,Gustaf Forsberg,Vincent Steinmetz,Pierre Grenier,Véronique Bellon-Maurel. Journal of Food Engineering . 1998 (2)

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