基于灰色模糊贝叶斯网络算法的编队突防作战效能评估方法  

Method of formation penetration operation effectiveness assessment based on gray fuzzy Bayesian network algorithm

作　　者：赵彬[1,2] 周中良[1] 阮铖巍[1] 穆中林[1] 

机构地区：[1]空军工程大学航空航天工程学院 [2]中国人民解放军95980部队

出　　处：《计算机应用》2017年第A01期356-360,364,共6页journal of Computer Applications

摘　　要：针对环境、人员素质等不确定性因素对编队突防作战效能的影响,提出一种改进灰色模糊贝叶斯网络(GFBN)的编队突防作战效能评估方法。首先,提出了基于层次分析第二类极大似然估计(AHP-ML II)模型的先验信息融合算法,将不同来源的历史先验信息加以处理,降低了主观性影响;其次,对贝叶斯网络模型的输入信息进行了灰色模糊地概率化转化处理;最后,针对不确定性因素的影响建立了基于改进GFBN法的单机、编队突防评估模型;进行了融合算法和编队突防的算例分析,着重将模糊因素作为评估模型的输入来考虑效能评估的效果。最后,通过效能评估值的对比发现,此算法将此类不确定性因素输入算法模型,能使编队突防作战效能显著提高,仿真结果更接近实战效能。Considering the influence of uncertain factors on the operation efficiency of the formation penetration, the method based on Gray Fuzzy Bayesian Network（ GFBN） for operational effectiveness assessments of formation penetration was proposed. Firstly, a prior information fusion algorithm based on Analytic Hierarchy Process-II Maximum Likelihood estimation（ AHP-ML II）） model was proposed, which could deal with the prior information of different historical sources, and reduce the influence of subjectivity. Secondly, the input information of Bayesian Network（ BN） model was transformed into gray fuzzy probability. Finally, single and formation evaluation penetration model based on improved GFBN algorithm was established aiming at the impact of the uncertainly factors. Based on the example analysis of fusion and penetration in formation, the fuzzy factors were taken as the input of evaluation model to consider the effect of efficiency evaluation. Finally, the results of comparing effectiveness assessment show that the proposed algorithm can significantly improve the operation effectiveness of formation penetration by inputting such uncertainties to the algorithm model, and make the simulation results closer to actual operation.

关 键 词：编队突防 效能评估 灰色模糊因素 层次分析第二类极大似然估计 贝叶斯网络 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]