摘要
针对影响煤矿采掘工作面瓦斯突出因素的不确定性和复杂的非线性关系,在分析Dempster-Shafer(D-S)证据理论的基础上,提出瓦斯突出危险等级评判的多传感器数据融合方法;通过对传感器采集的待评判采掘面参数进行预处理,得出D-S证据理论各传感器对突出危险等级的信度函数分配,再利用D-S证据理论的合成规则得到融合后的信度函数分配,从而实现危险等级的评判。结果表明,该方法具有良好的适应性并能得到较高的精确度,在一定程度上提高了评判系统的性能。
Aimed at the uncertainty and the complicated non-linear relation between the coal gas outburst and its affecting factors, a data fusion method for evaluation of gas outburst risk level based on Dempster-Shafer( D - S) evidence theory was presented. Through preprocessing for evaluation of mining surface parameters acquired by sensors, the belief function as- signment of sensors to gas outburst risk level was gotten. The fusion belief function assignment was gotten by using D - S rule, and risk level of mining face was evaluated. Experimental results show that the model is reliable and precise, gas out- burst risk level can be accurately predicted with proposed method, and it can improve the performance of the evaluation system to a certain extent.
出处
《济南大学学报(自然科学版)》
北大核心
2017年第1期73-76,共4页
Journal of University of Jinan(Science and Technology)
基金
河南省重点科技攻关计划项目(102102210203)