基于模糊规则和动态蚁群贝叶斯网络的无人作战飞机态势评估

为解决无人作战飞机复杂环境下的态势评估难题，阐述了蚁群优化和贝叶斯网络基本原理和数学模型，设计了一种基于模糊规则和动态蚁群贝叶斯网络的无人作战飞机态势评估方法.该方法通过蚁群贝叶斯网络把不完备数据转换成完备数据，从而大大简化了学习的复杂度, 并保证算法能够向好的结构不断进化.利用模糊逻辑改进动态蚁群贝叶斯网络算法，引入基于模糊语言和规则的专家经验，结合单值评估结果与概率向量，评价了不同时刻无人作战飞机的行为能力等级，能够提高态势评估方法的智能性并应用于工程实际.通过仿真实验验证了该方法在解决复杂作战环境下无人作战飞机态势评估问题时的可行性和有效性.

UCAV situation assessment based on fuzzy rules and dynamic ant colony-Bayesian network

In order to solve the challenging problem of unmanned combat aerial vehicles(UCAV) situation assessment in complex environments, based on the introduction of ant colony optimization, Bayesian network and mathematical model, a hybrid fuzzy rules and dynamic ant colony-Bayesian network was proposed in efforts to examine the situation assessment of UCAVs. The incomplete data was converted into a complete data packet by using a dynamic ant colony-Bayesian network, which can greatly simplify the complexity of learning, and ensure that the algorithm evolves into good structure. The dynamic ant colony-Bayesian network algorithm was improved by using fuzzy logic. The expert’s experience was adopted in the form of fuzzy language and rules. The single value assessment results were combined with the probability vector to evaluate the capacity level of UCAVs at different times, increase the intelligence of situation assessment, and practicality of engineering application. A series of experiments verified the feasibility and effectiveness of the proposed hybrid method for situation assessment of UCAVs in the complicated combat environment.