邵良杉;王振;

• 1:辽宁工程技术大学系统工程研究所

摘要(Abstract)：

针对回采工作面瓦斯涌出量预测问题，采用因子分析降低高相关性指数的维数，将提取的2个主要因子与煤层倾角、工作面长度、相邻煤层之间的距离、相邻煤层的厚度作为输入数据，采用“试错法”和回归方法确定BP神经网络隐层节点个数，利用改进遗传算法优化BP神经网络的阈值与权重，构建回采工作面瓦斯涌出量预测模型.利用15组测量数据对预测模型进行训练，3组数据用作预测的测试样本.研究结果表明：基于因子分析与改进遗传算法优化BP神经网络（FA-IGA-BP）的回采工作面瓦斯涌出量预测模型最大相对误差为3.794%，平均误差为2.945%，该模型在处理高维数据时具有预测精度高，泛化能力强的特点，可以有效地预测瓦斯涌出量.

关键词(KeyWords)： 回采工作面;瓦斯涌出量;因子分析;改进遗传算法;BP神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（71771111）

作者(Authors): 邵良杉;王振;

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