[1]刘少刚,吕建伟,王士成.基于数据融合与神经网络的火灾探测研究[J].应用科技,2011,38(05):9-12.[doi:10.3969/j.issn.1009-671X.2011.05.03]
 LIU Shaogang,LV Jianwei,WANG Shicheng.A primitive study of fire detection method based on data fusion and BP neural network[J].Applied science and technology,2011,38(05):9-12.[doi:10.3969/j.issn.1009-671X.2011.05.03]
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基于数据融合与神经网络的火灾探测研究(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第38卷
期数:
2011年05期
页码:
9-12
栏目:
机电工程
出版日期:
2011-05-05

文章信息/Info

Title:
A primitive study of fire detection method based on data fusion and BP neural network
文章编号:
1009-671X (2011) 05-0009-04
作者:
刘少刚吕建伟王士成
(哈尔滨工程大学 机电工程学院,黑龙江 哈尔滨 150001)
Author(s):
LIU Shaogang LV Jianwei WANG Shicheng
(College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China)
关键词:
多传感器数据融合神经网络火灾预报
Keywords:
multi-sensor data fusion neural network fire prediction
分类号:
U692
DOI:
10.3969/j.issn.1009-671X.2011.05.03
文献标志码:
A
摘要:
海上平台由于其自身空间狭小、管线设备高度集中等特点,要求其火灾探测系统误报率低.目前,单一类型火灾探测器的误报率非常高,经研究发现同时探测多类型火灾因素可大幅度降低误报率.文中根据多传感器数据融合技术将火灾探测器所测数据进行融合,然后应用BP神经网络进行训练仿真,降低了火灾探测器的误报率,满足了海上平台火灾探测系统的要求.
Abstract:
The fire detection system of offshore platforms requires a low false alarm rate due to its features such as cramped interior and highly centralized equipment of the pipeline. Now, the false alarm rate of any single type of fire detection is very high. Researches showed that detecting multiple fire factors at the same time could greatly reduce the false alarm rate. Based on applying the integral techniques of multisensor data fusion to the fire detection sensors, this thesis has implemented the simulation of BP neural network to reduce the false alarm rate of the fire detection sensor,which meets requirement of the offshore platform’s fire detection.

参考文献/References:

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[4] 高强.基于模糊神经网络火灾智能报警系统的研究[D]. 沈阳:沈阳航空工业学院, 2007: 10-31.
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[6] 张德丰. MATLAB神经网络应用设计[M].北京:机械工业出版社, 2009(1): 24-49.
[7] 缪燕子. 多传感器信息融合理论及在矿井瓦斯突出预警系统中的应用研究[D].徐州:中国矿业大学, 2009: 60-74.

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备注/Memo

备注/Memo:
基金项目:科技部社会公益基金专项资助项目(2005DIB3J138).
作者简介:刘少刚(1962-),男,教授,博士生导师,主要研究方向:机械电子工程、LNG船舶消防,E-mail:liushaogang@hrbeu.edu.cn.
更新日期/Last Update: 2011-06-22