基于SVR的煤矿安全资源与安全状态作用机理模型  

Mechanism Model for Coal Mine Safety Resources and Safety Status Based on Support Vector Regression

作　　者：王金凤[1] 翟雪琪[1] 冯立杰[1,2] 

机构地区：[1]郑州大学管理工程学院,河南郑州450001 [2]河南省煤层气开发利用有限公司,河南郑州450016

出　　处：《煤矿安全》2014年第6期222-225,共4页Safety in Coal Mines

基　　金：国家自然科学基金资助项目(71271194);河南省教育厅科学技术研究重点基金资助项目(13A630438)

摘　　要：针对煤矿安全资源与安全状态的强非线性特征,划分了煤矿系统安全资源和安全状态级别,确定了输入向量集合和输出向量集合,建立了基于支持向量回归机(SVR)的安全资源与安全状态的作用机理模型,并分别利用网格搜索算法(GS)和粒子群算法(PSO)对模型进行了参数寻优,确定了支持向量回归机模型,通过实例分析验证了模型的有效性及适用性。研究结果表明:PSO算法得到的最优参数作为模型,较好地拟合了安全资源与安全状态间的非线性复杂关系,且模型有效性和推广能力更强。Aiming at the strong nonlinear characteristic of safety resources and safety status in coal mine, this article divided the levels of coal safety resources and safety status, decided the sets of input vectors and output vectors and then established a mechanism model of safety resources and safety status based on SVR. It respectively used grid searching algorithm and particle swarm optimization to opti- mize model parameters for the best SVR model. An example verified the validity and applicabifily of the model. The results showed that the model using PSO algorithm fitted better the nonlinear relationship between safety resources and safety status, and this model also had a stronger effectiveness and generalization ability.

关 键 词：安全资源 安全状态 作用机理 支持向量回归机(SVR) PSO算法 寻优方法 

分 类 号：TD79[矿业工程—矿井通风与安全]