典型文献
Target threat estimation based on discrete dynamic Bayesian networks with small samples
文献摘要：
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior infor-mation missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is pro-posed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algo-rithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.
文献关键词：
中图分类号：
作者姓名：
YE Fang;MAO Ying;LI Yibing;LIU Xinrui
作者机构：
College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin 150001,China
文献出处：
引用格式：
[1]YE Fang;MAO Ying;LI Yibing;LIU Xinrui-.Target threat estimation based on discrete dynamic Bayesian networks with small samples)[J].系统工程与电子技术（英文版）,2022(05):1135-1142
A类：
DDBN
B类：
Target,threat,estimation,dynamic,Bayesian,networks,small,samples,accuracy,target,has,great,impact,command,decision,making,effective,way,deal,uncertainty,can,used,track,change,level,Unfortunately,traditional,problems,poor,learning,reasoning,environment,partial,prior,infor,missing,Considering,finiteness,discreteness,parameters,fuzzy,nearest,neighbor,algorithm,correlation,feature,quantities,CF,FKNN,Firstly,between,calculated,then,weight,introduced,fill,data,On,this,basis,reasonable,structure,constructed,by,using,expert,experience,complete,Simulation,results,show,that,accurately,when,seriously,improve,case,With,proposed,method,final,assessment,which,meets,needs,engineering,applications
AB值：
0.473152
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