基于模糊神经网络兖州矿区立井井筒非采动破裂的判别  

Judgment for non-mining fracture of shaft-lining in Yanzhou mine based on fuzzy neural network

作　　者：刘环宇[1] 王思敬[2] 曾钱帮[2] 胡波[2] 夏正义 

机构地区：[1]中国科学院武汉岩土力学研究所 [2]中国科学院地质与地球物理研究所,北京100029 [3]解放军总后后勤科学研究所,北京100071

出　　处：《岩土工程学报》2005年第10期1237-1240,共4页Chinese Journal of Geotechnical Engineering

基　　金：国家重点基础研究规划(937)资助项目(2002CB12702)

摘　　要：煤矿立井井筒非采动破裂是一种新型工程地质灾害。该灾害的发生与各影响因素之间的关系为非线性的、不确定的。本文采用模糊神经网络对其中隐藏的规律进行了提取与捕捉。经检验结果表明,通过模糊逻辑判别与人工神经网络的相结合能够很好的对立井井筒破裂灾害的发生进行判别,且结果准确可靠,能够满足实际要求。Non-mining fracture of shaft-lining is a new style of engineering geological hazard. The relationship between the developing of fracture and its factors of influence is non-linear and indeterminate. Adopting fuzzy neural network, hidden regularities for non-mining fracture of shaft-lining were extracted and captured. The verifying results indicated that the approach combining fuzzy logical judgment with artificial neural network could distinguish the developing of non-mining fracture of shaft-lining. The obtained results were accurate, reliable and could meet the practical requirements.

关 键 词：立井井筒 非采动破裂 模糊神经网络 模糊化 隶属度 

分 类 号：TU435[建筑科学—岩土工程]