Abstract: An arithmetic for fire identification is proposed based on composite of Rough Set Support Vector Machine（RS-SVM）.Firstly，using Rough Set theory，the six variables of fire characteristics mapped to the RS knowledge system are made，the redundant information is eliminated，the properties of the system are reduced，then the regulations of this knowledge system are acquired.By the generalization and nonlinear approach ability of SVM，the model using the regulations of this knowledge system is trained，ultimately，the accuracy and optimized fire identification algorithms are obtained.The simulation result indicates that the method has better performance of fire identification accuracy，converge speed，nonlinear approaching and immunity.

Key words: fire identification, Rough Set（RS）, Support Vector Machine（SVM）

摘要： 提出了一种基于粗糙集-支持向量机（Rough Set Support Vector Machine，RS-SVM）的火灾识别算法。首先利用粗糙集理论，将描述火灾特征的6个变量映射为粗糙集的知识系统，再去除冗余信息，对该系统进行属性约简，获取该知识系统的规则集；利用SVM泛化和非线性逼近能力，将以上规则集作为训练火灾识别SVM的样本集，最终得到分类准确、优化的火灾识别算法。实验仿真表明：该算法对火灾识别精度高、速度快、抗扰性好、非线性能力强，且适用范围广，对于火灾及时准确识别具有重要意义。

关键词: 火灾识别, 粗糙集, 支持向量机