基于BP神经网络的煤体结构类型判识模型研究  

Study of the Differentiating Model for Coal Structure Types Based on BP Neural Network

作　　者：李涛[1] 李辉[1] 王福忠[1] 

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《煤矿安全》2011年第11期19-22,共4页Safety in Coal Mines

摘　　要：针对矿井中在煤体结构类型细致辨识方面存在的难题,根据构造煤的结构特点及超声波在煤体中的传播特性和规律,并综合考虑波速、衰减系数等与煤体结构类型相关的参量,提出了一种以超声波反射法和BP神经网络为基础的煤体结构类型判识模型。实验证明该模型能够对煤体结构类型实现有效的判识,对煤与瓦斯突出灾害的预测具有重要的指导意义。In order to solve the difficulty in detail recognition about subdivision of coal structure types, a differentiating model that combined BP neural network with ultrasonic reflection method was brought forward. Coal structure types were recognized based on a reasonable consideration of ultrasonic speed, ultrasonic attenuation coefficient, characteristics of uhrasonie transmitting and other parameters relating to types of coal structure. It is significant for the improved ultrasonic differentiating model to forecast coal and gas outburst.

关 键 词：煤体结构类型 BP神经网络 超声波煤体衰减系数 超声波波速 

分 类 号：TD166[矿业工程—矿山地质测量]