IFS-BN结合的辐射源威胁评估方法

针对辐射源威胁评估实时性、复杂性和信息不确定性的问题,将直觉模糊集与贝叶斯网络相结合,提出一种基于IFS-BN的辐射源威胁评估方法。IFS-BN算法通过直觉模糊集刻画贝叶斯网络的节点信息,充分描述节点信息的确定性,不确定性以及犹豫度。同时,将直觉模糊集求得的各辐射源先验概率代入贝叶斯网络模型之中,得出后验概率进行威胁等级排序,并在此基础上建立了相应的评估指标体系。仿真分析表明,算法具有较好的可行性与有效性。与经典贝叶斯网络评估方法相比,IFS-BN算法的结果更加客观合理。

An Assessment Method of Emitter Threat Based on Intuitional Fuzzy Sets and Bayesian Network

Aimed at problems that there is a threat of emitter on timeliness, and complexity and imprecision exist in assessment process, a novel algorithm in assessment based on IFS-BN is proposed combined with Intuitionistic Fuzzy Sets (IFS) and Bayesian Network (BN). The IFS-BN algorithm fully describes determinacy, non-determinacy and hesitancy degree more adequately through the node information in Bayesian Network. On the basis of this, the algorithm substitutes priori probability calculated by Intuitional Fuzzy Sets into Bayesian Network model to obtain posteriori probability. Then, sequences of the threat level are acquired by posteriori probability. Meanwhile, corresponding evaluating index system of emitter threat assessment is established. The simulation shows that the algorithm is feasible and effective, and the results are much more objective and reasonable compared with the classical method of emitter threat assessment based on Bayesian Network.