摘要
针对煤矿单传感器和低层次的多传感器数据处理的单一、不确定性较高的缺点,在对监测信息进行分析、处理、维数压缩后,利用基于粗糙集理论与神经网络相结合的多传感器信息融合技术实现对瓦斯灾害信息的特征提取。通过对瓦斯灾害的辨识分析,获得更准确、更可靠的瓦斯状态信息,极大地提高了瓦斯监测系统的性能,为获得瓦斯灾害变化的一般规律和准确、及时地发布预测信息提供先进的理论和技术支持。
Aimed at the singleness and high uncertainty in data processing of single-sensor and low-lever multisensor in coal mine, the technology of information fusion based on the combination of the theory of rough set and neural network in muhi-sensor is adopted to realize the extraction towards the characteristics of gas disaster information, after analysis, processing and dimensionality compression to the detective information. With the help of the identification analysis of the gas disaster, the status information of gas is obtained more accurately and reliably and the performance of gas monitoring system is improved highly. Advanced theory and technical support are provided to obtain the general rules of variety of gas disaster and give out information accurately and timely.
出处
《传感器与微系统》
CSCD
北大核心
2009年第12期30-32,共3页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(50874059)
教育部博士点基金资助项目(200801470003)
辽宁省科技攻关计划资助项目(2007231003)
辽宁省优秀人才基金资助项目(2007R24)
关键词
多传感器
神经网络
信息融合
粗糙集
瓦斯监测
muhi-sensor
neural networks
information fusion
rough set
gas monitoring
