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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。
According to the strong non-linear characteristics of coal mine safety resources and safety status, the safety resources and safety status of coal mine are divided, and the set of input vectors and output vectors are determined. The safety resources and safety status based on support vector regression (SVR) (GS) and Particle Swarm Optimization (PSO) respectively, the parameters of the model are optimized and the support vector regression model is determined. The effectiveness and applicability of the model are verified by an example analysis . The results show that the optimal parameters obtained by the PSO algorithm can be used as a model to better fit the nonlinear complex relationship between safety resources and safety status, and the validity and extension ability of the model are stronger.