Application of Adaboost integrated BP neural network in detecting system of sensor array

HONG Lei; GONG Xue-fei; SUN Shou-tong; JIAN Jia-wen

doi:10.13873/J.1000-9787(2015)04-0148-03

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Application of Adaboost integrated BP neural network in detecting system of sensor array

更新时间：2025-09-11

- Application of Adaboost integrated BP neural network in detecting system of sensor array
- Vol. 34, Issue 4, Pages: 148-150+154(2015)
- 作者机构：
  
  宁波大学信息科学与工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2015)04-0148-03
  CLC： TP212;TP274
- Published：2015
- 稿件说明：
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[1]洪磊,龚雪飞,孙寿通,简家文.Adaboost集成BP神经网络在传感器阵列检测系统中的应用[J].传感器与微系统,2015,34(04):148-150+154.

HONG Lei, GONG Xue-fei, SUN Shou-tong, et al. Application of Adaboost integrated BP neural network in detecting system of sensor array[J]. 2015, 34(4): 148-150+154.
[1]洪磊,龚雪飞,孙寿通,简家文.Adaboost集成BP神经网络在传感器阵列检测系统中的应用[J].传感器与微系统,2015,34(04):148-150+154. DOI： 10.13873/J.1000-9787(2015)04-0148-03.

HONG Lei, GONG Xue-fei, SUN Shou-tong, et al. Application of Adaboost integrated BP neural network in detecting system of sensor array[J]. 2015, 34(4): 148-150+154. DOI： 10.13873/J.1000-9787(2015)04-0148-03.

摘要

针对目前常见的多元有害气体检测问题

设计并搭建了一种基于传感器阵列和集成BP神经网络相结合的传感器阵列检测系统。在该系统中采用集成BP神经网络对传感器阵列的三种混合有害气体的响应信号进行回归分析。为了提高集成BP神经网络的预测准确性

又利用Adaboost算法对集成BP神经网络进行了优化。结果显示:该系统能够准确地检测气体组分

通过Adaboost算法对集成BP神经网络优化后

预测的平均相对误差小于2%

能够有效解决气体传感器的交叉敏感问题

提高传感器的选择性。

Abstract

Aiming at problem of harmful gas mixture detection

a kind of gas detecting system is developed by combining sensor array with integrated BP neural network. The purpose of this test system is regression analysis on response signal of three harmful gas mixture measured by sensor array using BP neural network algorithm. In order to improve the prediction accuracy of the BP neural network

adopt the Adaboost algorithm to optimize integrated neural network. The results show that the system can accurately detect gas component

after integrated neural network is optimized by Adaboost algorithm

predicted average relative error is less than 2%

it can significantly solve problem of cross sensitivity and improve selectivity of sensor.

关键词

Keywords

references

Mn掺杂纳米SnO 2 的气敏性能研究 [J]. 梅军鹏,张覃轶,韩雪亮. 传感器与微系统 . 2013(01)

BP神经网络性能与隐藏层结构的相关性探究 [J]. 杨守建,陈恳. 宁波大学学报(理工版) . 2013(01)

苯甲酸溶胶—凝胶法制备ZnFe 2 O 4 气敏材料 [J]. 娄向东,张宁,王晓兵,李新莉,王学锋. 传感器与微系统 . 2009(02)

一种基于电子鼻的食醋识别新方法 [J]. 张覃轶,胡伟,叶卫平,孙伟,姜栎,徐海维. 传感器与微系统 . 2008(06)

A memetic particle swarm optimization algorithm for multimodal optimization problems [J] . Hongfeng Wang,Ilkyeong Moon,Shenxiang Yang,Dingwei Wang. Information Sciences . 2012

Cr 2 O 3 -modified ZnO thick film resistors as LPG sensors [J] . D.R. Patil,L.A. Patil. Talanta . 2008 (4)

Correlation of sensing behavior of mixed potential sensors with chemical and electrochemical properties of electrodes [J] . Nicholas F Szabo,Prabir K Dutta. Solid State Ionics . 2004 (3)

Combustion control and sensors: a review [J] . Nicolas Docquier,Sébastien Candel. Progress in Energy and Combustion Science . 2002 (2)

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