计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (14): 236-241.

• 工程与应用 • 上一篇    下一篇

一种新的瓦斯监测证据合成方法

陈  强,黄丹丹,李  彬,卢  愿   

  1. 江西理工大学 电气工程与自动化学院,江西 赣州 341000
  • 出版日期:2015-07-15 发布日期:2015-08-03

Novel evidence combination algorithm for gas monitoring

CHEN Qiang, HUANG Dandan, LI Bin, LU Yuan   

  1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • Online:2015-07-15 Published:2015-08-03

摘要: 煤矿瓦斯监测中,利用Dempster-Shafer证据合成方法实现多传感器信息融合可以提高系统整体决策和预警能力。根据煤矿安全规范设定区域危险等级,使用云模型建立危险等级属性隶属度曲线簇,输入传感器检测量提取各属性隶属度作为D-S融合的基本概率赋值。为了实现高度冲突证据合成,提出D-S与加权平均法混合的分步证据合成算法。仿真结果表明提出的算法合成高度冲突证据时,具有令人满意的收敛效果。

关键词: 瓦斯监测, 信息融合, 云模型, D-S证据合成

Abstract: Dempster-Shafer evidence combination can improve overall decision and early warning capability in coal mine gas monitoring system. The danger levels of local region in coal mine are defined according to coal mine safety specification. Cloud model is used for generating the curve clusters of the membership degrees corresponding to the danger levels. The membership degrees extracted from the curve clusters are regarded as basic probability assignment for D-S evidence combination. A step by step combination algorithm is put forward. In this algorithm, D-S method and the weighted averaged method are mixed for severe conflicting evidence combination. The simulation results show that proposed algorithm has satisfactory convergence effect in severe conflicting evidence combination.

Key words: gas monitoring, information fusion, cloud model, D-S evidence combination