基于遗传模糊树的海空对抗无人机智能决策模型

随着人工智能技术的应用领域不断扩大, 无人机(unmanned aerial vehicle, UAV)的自主智能化发展成为目前军事领域的研究热点。其中, UAV的武器智能决策能力是其面临舰艇编队剧烈火力打击时能否顺利完成对海侦察任务的关键条件。针对这一军事应用难点, 利用改进的遗传模糊树构建UAV武器智能决策模型, 以此实现UAV的智能武器决策: 通过设计三模糊子集参数编码法改进参数编码方式, 克服了无效编码导致的系统紊乱; 采用单、组合场景结合的训练方法改进种群训练方式, 提高了决策模型在多样化实战中的适应性。仿真结果表明，所提武器智能决策模型具有较好的有效性和灵活性。

Intelligent decision-making model by unmanned aerial vehicles in sea-to-air confrontation based on genetic fuzzy trees

Owing to the continuous expansion of artificial intelligence technology, the development of autonomous intelligence of unmanned aerial vehicle (UAV) has become a research hotspot in the military field. Intelligent weapon decision-making ability is essential for UAV to successfully complete a reconnaissance task in the face of severe fire attack from warships. In view of this difficulty in military application, the improved genetic fuzzy tree is used to build a UAV intelligent weapon decision model, so as to realize the intelligent weapon decision of UAV. The parameter encoding method is improved by designing a parameter encoding method for three fuzzy subsets, thereby overcoming the system disorder caused by invalid encoding. The training method of combining single and combined scenes is used to improve the population training mode, which subsequently improves the adaptability of the decision-making model in diversified actual combat. The simulation results show that the proposed intelligent decision model of weapons has good effectiveness and flexibility.