联合信度推理证据网参数学习  

Parameters learning in evidential network deducing using unite-believe value

作　　者：李贤[1] 陈志刚[1] 徐世友[2] 井沛良[2] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]国防科技大学ATR重点实验室,长沙410073

出　　处：《控制与决策》2014年第8期1419-1423,共5页Control and Decision

基　　金：国家自然科学基金项目(61073186;61379057);教育部博士点基金优先发展领域课题(20120162130008)

摘　　要：证据网络是证据理论的层次化推广,联合信任测度是证据网络中的一种推理方法,推理参数一般由专家经验给出.为了更准确地获得参数,提出一种将冲突大小作为评价函数,利用格雷编码遗传算法,通过评价输入来自动获取优化参数的方法.以机载多传感器平台为例的仿真实验表明,所提出方法的融合识别性能优于专家知识设定参数的方法,而且初始种群数量越大,越能获得好的识别结果.Evidential network is a reasoning model extend from Demspter-Shafer（DS） theory. Unite-believe value（UBV） is a way of deducing in evidential network. In a general way, the parameters in the deduction are given by expert knowledge. A method of getting optimized parameters automatically is presented. In which the value of conflict is used as adjudge function and the genetic algorithm based on gray code is used. The simulation experiment shows that the result of the proposed method is more accurate than the result of the method using the parameters given by expert knowledge in emulation, and the more population at beginning, the better accuracy of the recognition result.

关 键 词：证据网 联合信任测度 格雷编码 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]