基于SAGA-FCM的煤与瓦斯突出预测方法

为提高模糊C-均值聚类（Fuzzy C-Means Clustering Algorithm，FCM）算法在煤与瓦斯突出预测中的准确度，提出一种将模拟退火算法（Simulated Annealing Algorithm，SA）与遗传算法（Genetic Algorithm，GA）相结合用于模糊C-均值聚类分析的煤与瓦斯突出预测方法。该方法综合了模拟退火算法全局搜索、高精度的优点和遗传算法强大的空间搜索能力，将经遗传模拟退火算法优化后的初始值赋给FCM，避免了由于聚类中心初始值选择不当造成FCM算法收敛到局部极小点上。结合典型突出矿井数据进行分析，结果表明：遗传模拟退火算法优化后的FCM算法较单一，预测准确度高。

Prediction method of coal and gas outburst based on SAGA-FCM

In order to improve the accuracy of Fuzzy C-Means Clustering Algorithm (FCM) in prediction of coal and gas outburst, a method was putted forward. The method uses fuzzy C-means clustering that combines simulated annealing algorithm (SA) and genetic algorithm (GA) to predict coal and gas outburst. The method combines global search, high-precision advantages of simulated annealing algorithm and powerful search ability of space of genetic algorithm. Initial value optimized by the genetic simulated annealing algorithm is assigned to FCM. It avoids FCM algorithm convergence to local minima when the initial cluster center value is not properly selected in combination with the analysis of typical outburst mine data, the results indicate that FCM optimized by the genetic simulated annealing algorithm is more accurate than the single FCM algorithm in prediction of coal and gas outburst.