基于神经网络的火灾烟雾识别方法

提出了一种基于神经网络的火灾烟雾识别方法，以波长为670nm、1060nm、1550nm的三束激光的三对消光系数比作为网络的输入，网络的输出为“火灾烟雾”和“非火灾因素”，从典型火灾烟雾和非火灾因素对多波长激光的衰减实验中选取数据，组成26种网络样本模式定义表，经391次仿真训练后，输出误差小于0．0001，并经验证实验表明，本方法对火灾烟雾和非火灾因素能进行有效的识别，是处理烟雾识别等非结构问题的一种行之有效的方法。

An Identification Method of Fire Smoke Based on Neural Network

An identification method for fire smoke based on a neural network is discussed. The neural network′s input used three couples of extinction coefficient ratio of three laser beams with wavelengths of 670 nm, 1060 nm and 1550 nm respectively. The network′s output used fire smoke and non fire smoke factors. Experiments of multi wavelength lasers attenuation by typical fire smoke and non fire smoke factors were conducted, resulting in twenty six network sample pattern definition tables acquired from selected data. Three hundred and ninety one times of simulation training resulted in an output error less than 0.0001. The verification experiments prove that this method is effective in distinguishing between fire smoke and non fire smoke factors. This identification method has practical application for solving fire smoke recognition and other similar non structural problems.