贝叶斯判别法在煤与瓦斯突出预测中的应用

煤与瓦斯突出影响因素多,难以为其建立合适的多指标非线性预测模型,为提高突出预测的准确性和增强预测预报方法的实用性,文章选用瓦斯压力、瓦斯放散速度等多项影响判别因子,建立了基于贝叶斯判别分析煤与瓦斯突出的判别模型,通过对22组样本的学习和6组样本的预测,验证了该方法在煤与瓦斯判别中具有较高的准确性.

Bayesian discrimination analysis in coal and gas outburst prediction

The factors affecting coal and gas outburst are so many that it is difficult to establish an appropriate non-linear prediction model with many indicators for it．In order to improve the accuracy of forecast and enhance the practicability of prediction method，a coal and gas outburst prediction model was established by Bayesian discrimination suing gas pressure, gas diffuse rate and number of impact factors. Through the study of twenty-two samples and forecast of six samples, this method is verified with high accuracy in the coal and gas discrimination