CAJ | 학술논문

经过研究发现，火灾的发生机率具有双重性，即随机性与不确定性。而运用火灾自动报警系统检测火灾信号，就是将不确定的一面转化成比较准确的一面。基于此，利用BP神经网络算法计算和探测火灾图像的形成规律和信号特征，给出神经网络的具体结构和输入输出单元的设计方案。并对一系列的火灾样本图像和干扰图像进行实验。研究结果表明：此方法能更有效地减少火灾的误报警率，提高火灾报警的准确率。
경과연구발현，화재적발생궤솔구유쌍중성，즉수궤성여불학정성。이운용화재자동보경계통검측화재신호，취시장불학정적일면전화성비교준학적일면。기우차，이용BP신경망락산법계산화탐측화재도상적형성규률화신호특정，급출신경망락적구체결구화수입수출단원적설계방안。병대일계렬적화재양본도상화간우도상진행실험。연구결과표명：차방법능경유효지감소화재적오보경솔，제고화재보경적준학솔。
As for the duality of fire,namely randomness and determinism,the intelligent building automatic fire alarm system has the important task that transforms the randomness accurately to the another side with relative se-curity in the fire detection.Therefore,in this paper,the BP neural network algorithm was used to calculate and detect the developing rule and signal feature of the fire image.It gives the detailed structure of the BP nerve net and the concrete design scheme of input and output layers.Then a series of sample images of fire and interfer-ence images have been experimented.Experimental results show that fire detection algorithm based on the BP neural network is more effective to reduce the fire false alarm rate,and improves the accuracy of the fire alarm.