中文摘要：

为准确预测冲击地压危险性,提出一种优化Bagging算法动态集成的最小二乘支持向量机（LSSVM）的预测模型。在设计和优化Bagging-LSSVM模型流程的基础上,引入经典分类数据集,验证模型的可行性,并通过试验得出实现模型最优分类条件下的基分类模型数的最小值。综合考虑冲击地压的主要影响因素,确定其评判指标;以重庆砚石台煤矿的35组实测数据为试验样本,利用核主成分分析（KPCA）消除指标间的相关性,对比分析样本数据处理前后应用模型的预测效果;比较优化Bagging-LSSVM模型、优化Bagging-SVM模型和LSSVN模型预测冲击地压危险性的准确率。结果表明：经KPCA处理后的样本相较于原始样本,其应用于优化Bagging-LSSVM模型的预测准确率更高,耗时更少;且优化Bagging-LSSVM模型预测冲击地压危险性的准确率高于其他模型。

英文摘要：

To predict rock burst risk classification accurately,an optimized Bagging-LSSVM prediction model was built. On the basis of designing and optimizing the algorithm flow of Bagging-LSSVM,a set of classical classification datasets was introduced to the experiment. A minimum number was obtained experimentally for basic classification model ＇s number in meetting optimal classification. Main factors affecting rock burst were identified to futher determine the evaluation indexes. Then 35 groups of measured data provided by the Chongqing Yanshitai mine were used,as samples to test. Correlations among original sample indexes were eliminated by using KPCA. Then the rock burst prediction accuracy comparison was made among the optimized Bagging-LSSVM model,optimized Bagging-SVM model and LSSVM model. It is turned out that forecasting accuracy by the optimized Bagging-LSSVM model is greater than those by others.