Abstract: A new method using Support Vector Machine（SVM） which is optimized by Modified Particle Swarm Optimization（MPSO） to predict loosening zones around roadway is proposed, avoiding shortcomings of current determining methods. Compression factor is introduced to standard PSO. Effective balance between global search and local optimization is achieved. MPSO is applied to optimizing the SVM parameters C and g, and MPSO-SVM regression prediction model is established. The model is applied to predicting loosening zones around roadway. The prediction result is compared with PSO-SVM, GA（Genetic Algorithm）-SVM, GSM（Grid Search Method）-SVM, BP neural network. The results show that the model has better generalization performance and higher prediction accuracy. It can effectively predict loosening zones around roadway.

Key words: Particle Swarm Optimization（PSO）, Support Vector Machine（SVM）, loosening zones around roadway, simulation and forecast

摘要： 针对目前巷道围岩松动圈确定方法的种种缺陷，提出了一种新的预测方法，采用改进的粒子群算法（MPSO）优化支持向量机（SVM）对巷道围岩松动圈进行预测。在标准PSO中引入压缩因子，实现了算法全局搜索和局部寻优的有效平衡；应用MPSO对SVM的参数C和g进行优化，建立MPSO-SVM回归预测模型；将该预测模型应用于巷道围岩松动圈的预测，将预测性能与PSO-SVM、GA（遗传算法）-SVM、GSM（网格搜索）-SVM模型、BP神经网络进行对比分析。结果表明：该模型具有较强的泛化能力，较高的预测精度，可以对围岩松动圈厚度进行有效预测。

关键词: 粒子群算法, 支持向量机, 围岩松动圈, 仿真预测