Recognition of Fire Detection Based on Neural Network
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 LevenbergMarquardt 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.
fire detection BP neural network RBF network
Yang Banghua Dong Zheng Zhang Yonghuai Zheng Xiaoming
Shanghai Key Laboratory of Power Station Automation Technology,Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China
国际会议
无锡
英文
250-258
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）