GIBBS仿真方法运用在大型因果图的推理过程  

The Application of GIBBS Sampling in the Reasoning of Huge Causality Diagram

作　　者：汪成亮[1] 陈娟娟[2] 

机构地区：[1]重庆大学计算机学院,重庆400044 [2]重庆师范大学物理学与信息技术学院,重庆400047

出　　处：《计算机工程与应用》2006年第13期26-29,共4页Computer Engineering and Applications

基　　金：重庆市自然科学基金资助项目(合同号:CSTC2005BB2189)

摘　　要：在信度网基础上发展起来的因果图模型,克服了信度网的一些不足,目前已发展成一个能够处理离散变量和连续变量的混合模型,特别适于运用在大型的工业故障诊断领域。但因果图在推理计算中,面临着与信度网的推理算法复杂度过高的同样问题。通过比较几种MarkovChainMonteCarlo(MCMC)方法,论文基于Gibbs仿真算法的思想,并对进入稳态条件、采样顺序判定准则、采样结束判据等进行深入分析,进而提出了一种改进的仿真推理新方法。利用该采样仿真算法能极大地提高故障诊断的速度和准确度,这对因果图模型在工业在线故障诊断领域中的应用具有重要意义。The causality diagram methodology,which is based on belief network,overcomes some shortages in knowledge expressing and reasoning of belief network and has evolved into a mixed causality diagram methodology coping with discrete and continuous variables,and it is very useful for industrial fault diagnosis application.However,it is still confronted with a problem as belief network is,of high computation complexity.By comparing several Markov Chain Monte Carlo（MCMC） simulating algorithms,and analyzing the requirement for stable-condition,the principle of sampling sequence and the criterion of sampling ending,this paper puts forward an improved simulating reasoning algorithm based on Gibbs simulation.The simulating algorithm will improve the diagnosis speed and accuracy,which has an important significance for the application in industrial online fault diagnosis.

关 键 词：因果图 信度网 Gibbs仿真 故障诊断 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]