【作者】 边宝峰； 马平；

【Author】 BIAN Bao-feng, MA Ping(Deparment of Automation,North China Electric Power University, Baoding 071003, China)

【机构】 华北电力大学自动化系；

【摘要】 本文针对D-S证据理论对于相关性信息融合算法的不足,采用了基于D-S证据理论的关联与非关联融合算法;并利用模糊神经网络技术把环境信息和引入融合系统,同时采用时间融合和空间融合,提出了一种新的信息融合算法。将该方法应用到火电厂磨煤机存煤量的测量上。结果表明,新的融合算法能较大程度地提高运算速度,并具有较强的抗干扰能力和较高的可靠性。更多还原

【Abstract】 Aiming at the limitation of traditional evidential theory, This paper uses a fusion arithmetic about relevancy based on D-S evidential theory. And applies the fuzzy neural network combined entironmcntal information, we put forward a new information arithmetic with regard to spatio as well as temporal information fusion. At last we applied this method to the measure of the coal mass of pulverrizer. The fusion result showes that the new fusion arithmetic han greatly improved the operational speed and brings the whole system with more anti-jamming and high reliability.更多还原