CAJ | 학술논문

现阶段，神经网络模型在火灾探测信息处理应用中存在以下缺陷：选取火灾特征组合具有主观性；选取的神经网络类型缺乏对比；缺乏大量实验数据对神经网络泛化能力的验证。利用 NIST 机构所做一系列火灾探测研究实验数据样本，通过信息熵理论在火灾信号选取中的应用获取火灾复合探测信号特征选取的组合形式，并在此基础上建立火灾探测信息处理神经网络初始模型。经过一系列 Matlab 仿真实验，分析神经网络的模型结构、传递函数和训练函数对仿真结果的影响，提出一种基于 trainbr 训练函数、tansig 传递函数的3－7－1结构 BP 神经网络模型。采用网络训练时间、探测点、误报率和网络输出区间进行网络性能分析，验证所提出模型在火灾探测中应用具有训练速度快，结果稳定可靠，探测灵敏的特点。
현계단，신경망락모형재화재탐측신식처리응용중존재이하결함：선취화재특정조합구유주관성；선취적신경망락류형결핍대비；결핍대량실험수거대신경망락범화능력적험증。이용 NIST 궤구소주일계렬화재탐측연구실험수거양본，통과신식적이론재화재신호선취중적응용획취화재복합탐측신호특정선취적조합형식，병재차기출상건립화재탐측신식처리신경망락초시모형。경과일계렬 Matlab 방진실험，분석신경망락적모형결구、전체함수화훈련함수대방진결과적영향，제출일충기우 trainbr 훈련함수、tansig 전체함수적3－7－1결구 BP 신경망락모형。채용망락훈련시간、탐측점、오보솔화망락수출구간진행망락성능분석，험증소제출모형재화재탐측중응용구유훈련속도쾌，결과은정가고，탐측령민적특점。
In the processing of fire detection,some defects are contained in the use of neutal network model. These defects are the subjectivity of the selection of fire feature,the lack of comparison among the selected neural network types and the lack of adequate experiments.Based on a series of data samples of fire detection experi-ments given by NIST,the combination of signal feature selection for composite fire detection through the applica-tion of information entropy theory in the selection of fire signal is obtained,and the initial model of neural net-work for the fire detection information processing is established.After a series of exploratory experiments simula-ted by Matlab,the effects of the structure of the neural network model,transfer function and the training function on the simulation results are analyzed,and an improved BP neural network model based on trainbr training func-tion,tansig transfer function and the structure of 3 -7 -1 is proposed found.Through the network performance analysis of network training time,probe points,the false positive rate,and network output interval,the proposed model is fast,reliable and senstitive in fire detection.