Recognition of Fire Detection Based on Neural Network

被引：0
|
作者：
Yang Banghua [1 ]
Dong Zheng [1 ]
Zhang Yonghuai [1 ]
Zheng Xiaoming [1 ]
机构：
[1] Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Dept Automat, Coll Mechatron Engn & Automat, Shanghai 200072, Peoples R China
来源：
LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING, PT II | 2010年 / 6329卷
关键词：
fire detection; BP neural network; RBF network;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming to the fire detection, a fire detection system based on temperature and pyroelectric infrared sensors is designed in this paper. According to the National Fire Detection Standard, a great number of test data are acquired. A model based on Levenberg-Marquardt Back Propagation (LM-BP) neutral network is established to recognize the fire status using the acquired data. Among the data, 200 groups of samples are used to train the established LM-BP networks while 1500 groups of samples test the LM-BP model. A 90% recognition rate is obtained by the LM-BP model. Compared with the other neutral networks such as Radial Basis Function (RBF) network, the LM-BP neural network has a significantly higher recognition rate (90%) than the RBF net (70%). The initial results show that the LM-BP recognition method has a favourable performance, which provides an effective way for fire detection.
引用
收藏
页码：250 / 258
页数：9
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