针对目前火灾探测技术难以满足实际需要的问题，在分析RBF 网络结构特点及最近邻聚类学习算法的基础上，提出用RBF 神经网络建立火灾探测器模型，以火灾初期实验得到的环境温度、烟雾浓度、CO 含量为输入，以明火概率、阴燃火概率、无火概率为输出对RBF 网络进行训练，并进行仿真试验，结果表明，实际输出与期望输出的相差较小。关键词：径向基函数（RBF）；神经网络；最近邻聚类算法；火灾探测器模型Abstract: In view of the problem that presently, the fire detective technology difficultly meet the actual needs, and on the basis of analyzing the characteristic of RBF network structure and the Nearest Neighbor-Clustering Algorithm, there comes a proposal that to use the RBF neural network to establish fire detector model .Taking the ambient temperature, the smog density, the CO content which obtains by the experiment of fire initial period as the input, and flame probability, glowing fire probability and misfire probability as the output. Researchers carry on the training to the RBF network. Meanwhile, they carry on the simulation experiment. As a result, there is little difference between the actual output and expected output.Key word: Radial Basis Function (RBF), Neural networks ，Nearest Neighbor-ClusteringAlgorithm, Fire detector model
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