摘要
通过分析深井底板突水因素的影响作用,建立各影响因素的层次分析结构模型,运用层次分析法计算各影响因素的权重并进行排序,进而选出深井底板突水的主控因素。在该基础上,建立隶属度和隶属函数实现各因素的归一,构建基于模糊神经网络的深井底板突水判别模型,选择合适的网络结构参数以改善神经网络的缺点,并选取样本训练网络,以现场实例为验证样本,以突水等级作为输出结果,该判别表明基本符合工程实践。
Through analyzing the influencing factors of water inrush from deep-well floor, a hierarchic analysis structure model is established. And then, the weights of the influencing factors are calculated and arranged in order by using analytic hierarchy process （AHP）, through which the major influencing factors are decided. On this basis, the factors are normalized through setting up the membership function and membership grade, and a discrimination model for water inrush is constructed based on fuzzy neural net-work （FNN）. By choosing appropriate network structure parameters and training samples, this model, with the grade of water inrush as the output, achieves the learning process. Finally, this model is verified with field examples, which shows that the result obtained by the model is basically in accordance with the engineering practice.
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2009年第12期61-65,共5页
China Safety Science Journal
基金
国家自然科学基金资助(50874070)
关键词
深井底板突水
层次分析法(AHP)
隶属函数
模糊神经网络(FNN)
判别
water bursting from deep-well floor
analytic hierarchy process（AHP）
membership function
fuzzy neural network（FNN）
distinguish
