摘要
针对井下信息量大、噪声多、参数多、动态等特征,提出了一种基于粗糙集数据挖掘和D-S证据理论优化信息融合技术的矿井环境监测方法。采用粗糙集对井下信息进行预处理;利用径向基函数(RBF)神经网络建立了井下环境识别模型;利用D-S证据理论进行两级融合决策,并对井下安全状况进行判断。仿真结果表明:该方法提高了井下信息的识别和决策效果,极大地降低了不确定性。
Aimed at the characteristics of coal mine such as large quantity of information, much noises, many parameters and dynamic characteristics, etc. A coal mine environmental monitoring method of information fusion technology optimized by rough set data mining and D-S evidence theory are conducted. The rough set was carried out to deal with the information of coal mine. Distinguishing model of coal mine environment was established by using the radial basic function （RBF） neural network. Two-graded fusion decision was conducted by using D-S evidence theory to judge the security situation underground. The simulation results show that this method improved the identification and decision-making effects of coal mine environmental information, which greatly reduced the uncertainty.
出处
《传感器与微系统》
CSCD
北大核心
2010年第6期18-20,24,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(50874059)
辽宁省重大科技计划资助项目(2007231003)
辽宁省优秀人才基金资助项目(2007R24)
辽宁省创新团队基金资助项目(2007T071)
关键词
粗糙集
RBF神经网络
信息融合
D—S证据理论
矿井环境监测
rough set （RS）
radial basic function （RBF） neural network
information fusion
D-S evidence theory
coal mine environmental monitoring
