中文摘要：

主要影响角正切tanβ是采用概率积分法进行矿山开采沉陷预计的主要参数之一,决定着开采沉陷的影响范围。为了提高tanβ求取精度,在分析tanβ及其影响因素的基础上,选取tanβ的5个主要影响因素作为输入层神经元,将粒子群（PSO）快速搜索全局最优解算法与径向基（RBF）神经网络相结合,提出一种求取tanβ的PSO-RBF神经网络预测模型,获得tanβ和地质采矿条件之间的非线性映射关系。运用我国30个典型观测站的实测数据作为学习训练和测试样本,进行了PSO-RBF神经网络模型的适应度和泛化能力测试,对预测结果与实测值进行了对比分析。结果表明：应用PSO-RBF神经网络模型预测tanβ,收敛速度快,预测精度高。预测结果的最大相对误差为6.54%,最小为2.56%,所得到的tanβ精度有了一定的提高。

英文摘要：

The tangent of major influence angle tanβ is one of the most important parameters for mining subsidence prediction with the probability integral method,and it determines the influence range of mining subsidence. In order to improve calculating accuracy of tanβ,and based on analysis of tanβ and its influence factors,5 main influence factors on tanβ as inputting layer neuron are selected. Combining PSO algorithm of quick searching the global optimal solution with RBF neural network,a PSO-RBF neural network prediction model is proposed,and the nonlinear mapping relationship between tanβ and mining and geological conditions is obtained. Then,data from 30 typical observation stations are used as learning and training sample to test the fitness and generalization of PSO-RBF neural network model. The predication results of the PSO-RBF neural network and the observation values are analyzed and compared with each other. The results show that： adopting PSO-RBF neural network to calculate tanβ,the rate of convergence is rapid,with high prediction accuracy. The prediction result of maximum relative error is 6. 54%,the minimum relative error is 2. 56%,and the accuracy of tanβ is improved to some degree.