中文摘要：

为评价冲击矿压危险程度，提出一种基于粒子群算法和BP神经网络（PSO－BP）的冲击危险评估方法。利用已有冲击矿压数据，通过BP网络建立回归模型，并采用PSO算法对模型的连接权重和阀值进行优化，克服了BP网络收敛速度慢、易陷入局部最优的缺点。选取冲击矿压的10种主要影响因素，利用典型冲击矿井的20组工程数据建立PSO－BP评估模型，并将该模型与标准BP模型进行对比分析，结果表明PSO－BP模型较标准BP模型的评估准确率提高15％。最后，通过某矿冲击危险评估的工程实例验证了该方法的可行性和普适性。

英文摘要：

In order to evaluate rock burst risk degree, a new evaluation method based on the model of particle swarm optimization （PSO） algorithm and BP neural net is proposed. The method employs the technology of BP neural network to establish a regression model and takes advantage of PSO algorithm to optimize the parameters of the network, namely, the weights and the threshold values. All the data used in the model are from existing rock burst database. As a result, the BP neural network cannot fall into local optimum and its convergence speed can be increased. Then, ten major factors are selected as the inputs and twenty practical cases from a characteristic rock burst coal mine are chosen to establish the PSO-BP evaluation model. The newly established model is further compared with the standard BP model. Results show that the evaluation accuracy is increased by 15 percent by the proposed model in this paper. Finally, the proposed PSO-BP evaluation model is finally applied to evaluate the rock burst risk degree of two coalmines, where the feasibility and applicability of the method are verified.