具有分类功能的煤炭产量监测系统设计  

Design of Coal Production Monitoring System with Function of Classification

作　　者：王卫军[1,2] 郭江涛[1,2] 

机构地区：[1]中煤科工集团重庆研究院有限公司,重庆400039 [2]瓦斯灾害监控与应急技术国家重点实验室,重庆400039

出　　处：《中州煤炭》2016年第2期109-111,118,共4页Zhongzhou Coal

摘　　要：针对煤矿产量监测系统中煤炭与其他物资的区分能力不足、无法准确获得煤矿的实际产量等问题,提出了基于模糊神经网络算法的重量分类模型,并利用多传感器信息融合技术,自学习的方法不断提升模型识别煤炭能力及速度,获得更加精确的重量数据。数据仿真及现场应用表明,设计的称重系统能够很好识别煤炭并准确记录重量,为远程煤炭称重系统设计提供了一种思路。For coal and other materials in the production of coal mine monitoring system to distinguish ability is insufficient, can not ac- curately obtain actual production of coal mine,the weight classification model was put forward based on fuzzy neural network algorithm. By using muhi-sensor information fusion technology,the dentification ability and speed of model were improved by using the method of self-learning coal, so more accurate weight data was obtained. Through data simulation and field application, it was shown that the design of the weighing system is good for identification of coal and can accurately record the weight,and has provided a train of thought for the design of remote coal weighing system.

关 键 词：煤矿产量 模糊神经网络 信息融合 

分 类 号：TD76[矿业工程—矿井通风与安全]