Fire detection algorithm based on extension neural network

YAN Hao; WANG Ti-chun; HU Xin-xin; XIE Yu-zhu

doi:10.13873/J.1000-9787(2016)06-0113-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Fire detection algorithm based on extension neural network

更新时间：2025-09-11

- Fire detection algorithm based on extension neural network
- Vol. 35, Issue 6, Pages: 113-116(2016)
- 作者机构：
  
  南京航空航天大学机电学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)06-0113-04
  CLC： TP183;TP202
- Published：2016
- 稿件说明：
移动端阅览
[1]闫浩,王体春,胡欣欣,谢玉珠.基于可拓神经网络的火灾探测算法[J].传感器与微系统,2016,35(06):113-116.

YAN Hao, WANG Ti-chun, HU Xin-xin, et al. Fire detection algorithm based on extension neural network[J]. 2016, 35(6): 113-116.
[1]闫浩,王体春,胡欣欣,谢玉珠.基于可拓神经网络的火灾探测算法[J].传感器与微系统,2016,35(06):113-116. DOI： 10.13873/J.1000-9787(2016)06-0113-04.

YAN Hao, WANG Ti-chun, HU Xin-xin, et al. Fire detection algorithm based on extension neural network[J]. 2016, 35(6): 113-116. DOI： 10.13873/J.1000-9787(2016)06-0113-04.

摘要

为解决传统单一传感器式的火灾探测器容易造成火灾报警的漏报和误报的问题

采用多传感器信息融合技术

将温度、烟雾浓度和CO浓度等多个参数相结合

进行综合分析

对火灾进行早期预测。采用可拓神经网络作为数据融合算法

以温度、烟雾浓度、CO气体浓度三个物理参量作为输入

以三种火灾预警等级作为输出。通过仿真分析结果表明:火灾正确识别率很高

达到93.9%以上。同时通过与传统BP神经网络的对比

表明可拓神经网络在数据融合的速度和可靠性上有突出的优势

从而使可拓神经网络实际应用于火灾早期预测成为可能。

Abstract

In order to solve problem of failing or false alarm in traditional single sensor type fire detector

multisensor information fusion technology is applied to fire early prediction

it combines temperature

smog concentration and CO concentration together and analyze comprehensively. Extension neural network is used as data fusion algorithm

input values are temperature

smog concentration and CO concentration

and output values are three kinds of fire warning level. Simulation analysis result shows that correct identification rate of fire is in a very high level

which reaches above 93. 9 %. At the same time

compared with traditional BP neural network

it shows that extension neural network has a prominent advantage in speed of data fusion and reliability

so it is possible to apply extension neural network to fire early prediction.

关键词

Keywords

references

可拓神经网络研究综述 [J]. 周玉,钱旭,张俊彩,孔敏. 计算机应用研究 . 2010(01)

可拓神经网络模型的设计与实现 [J]. 孙佰清,邢爱国,张积宾,潘启树. 哈尔滨工业大学学报 . 2006(07)

多传感器/多判据探测器在火灾探测中的应用 [J]. 汤正华,王殊,陈涛. 传感器技术 . 2001(03)

一种基于前馈神经网络的火灾探测方法 [J]. 杨宗凯,王殊,何建华,沈春蕾. 华中理工大学学报 . 1997(02)

基于神经网络和模糊逻辑的智能火灾探测 [J]. 何建华,杨宗凯,王殊. 华中理工大学学报 . 1997(02)

可变窗信号趋势算法及其应用于火灾自动探测 [J]. 王殊. 华中理工大学学报 . 1996(11)

火灾自动探测的复合特定趋势算法 [J]. 王殊. 火灾科学 . 1996(01)

基于神经网络的多传感器数据融合火灾预警系统研究 [D]. 周晓琳. 长春理工大学 2012

基于小波神经网络的智能火灾探测研究 [D]. 陈月. 东北大学 2008

多传感器信息融合及应用[M]. 电子工业出版社 , 何友等著, 2000

Views

187

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Indoor pedestrian dead reckoning method based on multi-sensor data fusion

Obstacle avoidance of mobile robot based on multi-sensor information fusion

Multi-sensor data fusion algorithm based on measurement adaptive identification

Intelligent information fusion based on evaluation and decision of fuzzy preference relations

Obstacle avoidance study for mobile robot based on two-level sensor information fusion

Related Author

PENG Shi-yu

JIN Xue-bo

WANG Bin-bin

SU Ting-li

LIU Sheng-kai

CHEN Yi-mei

CHEN Shao-ru

DU Hai-shun1

Related Institution

School of Computer and Information Engineering,Beijing Technology and Business University

School of Electrical Engineering and Automation,Tianjin Polytechnic University

Institute of Image Processing & Pattern Recognition,Henan University

Basic Experiment Teaching Center,Henan University

School of Information Science and Engineering,Central South University

⁰