未来中远距协同空战多目标攻击决策研究

根据先进战斗机的技术特点和未来中远距协同空战发展方向,提出一种基于先敌发现、先敌发射、先敌摧毁能力的空战态势分析模型。同时,采用一种将并行遗传算法与分布估计算法相结合的并行分布遗传算法并将其用于多目标攻击决策。最后利用具体算例进行仿真。结果表明空战态势分析模型能够较为准确地描述未来中远距多机协同空战,采用的算法具有较好的可行性和实时性,为中远距多机协同空战的多目标攻击决策提供了新的思路和方法。     

NSHT预警装备体系网络化建模与分析

针对NSHT的运动特性和NSHT预警装备网络化趋势,对NSHT情报的分布式碎片化的特点进行了分析,设计了装备体系节点间的信息流向。在此基础上,提出了基于复杂网络理论的NSHT预警装备体系网络模型,并对网络模型的拓扑结构、统计特征等进行了研究,提出了NSHT预警装备体系连接结构的设计建议和应用思路。     

神经网络架构搜索研究进展与展望

神经网络架构搜索旨在针对不同任务,自动化地搜索得到性能最优的神经网络结构,是深度学习、计算机视觉技术结合当前现实需求应运而生的一大重要科学问题。对近年来神经网络架构搜索研究进行梳理、归类和评述;阐述神经网络架构搜索的定义和意义,全方位剖析当前研究所面临的难点与挑战;以此为基础,对主流的搜索策略进行阐述和归纳;探讨研究潜在的问题及未来颇具潜力的研究方向,以期推动该领域的进一步发展。     

利用循环网络的降落伞开伞载荷补偿方法

为了对降落伞充气展开过程中的开伞载荷进行更加准确的预测,提出一种基于循环神经网络的开伞载荷补偿计算方法,包括模型架构和数据处理方式。该方法将充气时间法计算的预测值代入循环网络进行二次计算,使最终结果能够更加贴近试验真值。使用多层前馈网络、标准循环网络与长短时记忆网络三种网络进行比较,验证了所提模型预测结果的适用性和准确性,研究了学习率、输入层维度和隐层维度等超参数对模型性能的影响,并给出了基于长短时记忆网络的补偿模型最优训练条件。实验结果表明,利用循环网络进行开伞载荷预测具有较好的拟合结果,为机器学习与降落伞工业的学科交叉研究提供了一定的参考方向。     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

Fitting Lanchester equations to the battles of Kursk and Ardennes

Lanchester equations and their extensions are widely used to calculate attrition in models of warfare. This paper examines how Lanchester models fit detailed daily data on the battles of Kursk and Ardennes. The data on Kursk, often called the greatest tank battle in history, was only recently made available. A new approach is used to find the optimal parameter values and gain an understanding of how well various parameter combinations explain the battles. It turns out that a variety of Lanchester models fit the data about as well. This explains why previous studies on Ardennes, using different minimization techniques and data formulations, have found disparate optimal fits. We also find that none of the basic Lanchester laws (i.e., square, linear, and logarithmic) fit the data particularly well or consistently perform better than the others. This means that it does not matter which of these laws you use, for with the right coefficients you will get about the same result. Furthermore, no constant attrition coefficient Lanchester law fits very well. The failure to find a good‐fitting Lanchester model suggests that it may be beneficial to look for new ways to model highly aggregated attrition. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

基于任务和角色的分布式工作流安全模型

针对现有基于角色访问控制的缺陷和分布式工作流管理系统的特性,在传统的基于角色的访问控制模型中引入任务集(Tasks)、任务实例集(TaskInstances)和任务上下文(TaskContext)的概念,将传统的user role permission权限赋予结构修改为user role task permission权限赋予结构,建立了基于任务和角色的访问控制模型,给出了其形式化定义。该模型解决了传统的基于角色访问控制中的动态适应性差和最小权限约束假象的问题,用于分布式工作流管理系统,提高了安全性、实用性。     

具有分流型抑制机制的视网膜细胞神经网络模型及其应用

视觉细胞之间存在的侧抑制机制能够对接收到的视觉信息进行选择性的提取。运用数学方法模拟视觉细胞之间存在的这种生理机制,提出了一种具有分流型抑制机制的视网膜细胞神经网络模型(SIRCNN),分析了该模型的稳定性,给出了等价电路图,并且基于此模型提出了一种边缘检测算法。将该算法应用于图像边缘检测中,试验结果表明,这种以生物视觉感知机理为基础的神经计算方法可有效地增强图像反差,检测图像边缘。由于细胞神经网络具有高速并行运算、便于硬件实现等特点,因此这种方法在图像实时处理中也具有很大的潜力和应用前景。     

基于排列法的目标威胁评估模型

目标威胁评估是一个推理、决策的行为,结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。     

模糊合成算子在作战方案综合评估的应用研究

在分布式交互仿真中,根据仿真结果对作战方案进行综合评估,运用模糊综合评判的方法进行评判,并着重对合成算子的选择进行分析,确定乘与有界算子M(·, )为比较适合对作战方案评估的算子.