基于功能树的导弹打击系统目标瞄准点选择方法

针对导弹打击系统目标的问题,建立了基于目标功能损伤度的瞄准点选择方法;在对毁伤树方法进行改进的基础上,提出了描述目标结构与工作流程的目标功能树模型,构造出体现目标整体功能下降的优化目标函数,采用模拟退火算法搜索最优解。通过算例对不同的瞄准点选择方案进行比较,发现考虑目标的功能结构特点时,得到的瞄准点明显优于其它方案。     

数据挖掘故障诊断专家系统

为了及时准确地对防空导弹飞行试验故障进行诊断,克服传统专家系统获取专家知识的瓶颈,立足于防空导弹飞行试验历史数据,利用数据挖掘的方法获得飞行试验故障模式判定决策树,进而得出导弹故障判定规则,形成故障诊断专家知识库,构建防空导弹故障诊断专家系统.这使数据挖掘和专家系统两者的优点得到充分发挥.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

面向无人环境的机器人生态圈自主任务决策方法

借鉴自然界生态系统的典型特征,提出机器人生态圈概念。通过使集群机器人进行智能协同与复杂演化,涌现自我维持、自我复制与自我进化等生命特征,实现无人条件下的长期生存、繁衍与进化,并执行特定的任务。针对机器人生态圈典型任务场景的自主任务决策需求,分析不同机器学习任务决策方法的特点,建立机器人生态圈自主任务决策的决策树模型和神经网络模型。分析表明,两种模型的正确率均在80%~90%,且均具有良好的稳定性。这说明,机器人生态圈自主任务决策问题可以通过决策树、神经网络等机器学习方法来很好地加以解决,从而为面向无人化场景的任务应用提供技术支持。     

作战飞机可靠性与维修性仿真模型

通过故障树分析建立了作战飞机仿真逻辑关系。在修正线性同余法的基础上获得了随机数模型。在各设备故障发生状态和修复状态识别的基础上,建立了可靠性维修性仿真模型,确定了作战飞机在仿真过程中的工作状态,并根据故障模式影响分析预计了设备故障对作战飞机的影响。最后以F/A-18为例进行可靠性维修性仿真,获得了实时的故障信息和修复信息,验证了仿真模型的可行性。     

舰船机械设备备件保障决策分析——可靠性分析

针对典型的舰船机械设备系统,运用故障树分析等现代可靠性理论分析方法,结合系统设备的技术性能分析、非电产品的结构和故障特点以及运行管理实际情况,研究系统设备、部件的故障机理,故障模式和故障影响效应,明确导致设备、部件故障的原因及发生模式。应用FTAS故障树分析软件包对其进行了定性、定量分析,获得了以概率结构事件、最小割集等形式表达的系统设备、部件故障对系统故障的作用模式,并对系统故障概率随时间的变化进行了拟合检验及评价了系统可靠性,为确定在指定远航任务期(90天)内所需的备品备件决策提供依据。     

基于YH-F2 系统的一般算术表达式的并行成分识别

本文深入研究了基于ＹＨ－Ｆ２系统的算术表达式的并行计算特点,提出了嵌套层次树的概念和相应的计算模式。这种模式充分利用了ＹＨ－Ｆ２系统的并行计算能力,完满地解决了基于ＹＨ－Ｆ２的算术表达式的并行编译问题     

从故障特征群中快速确定故障点的方法

工程设备的结构日趋复杂,使故障诊断的难度不断增大。本文在最大故障信息量诊断的基础上提出了最大故障信息增量判断准则,可从故障特征群中一次找出最可能的故障点,简化了诊断过程,提高了诊断速度。     

多UAV协同区域覆盖侦察方法

针对多UAV对未知区域的协同覆盖侦察问题,提出了将STC算法与在线局部调整策略相结合的区域覆盖侦察方法.通过建立无重叠覆盖区域的闭合路径,最小化了重复侦察区域,缩短了任务执行时间;通过在线局部调整保证了只要有一架UAV不出现故障,侦察任务就可以正常进行.仿真结果表明,该方法能够有效解决多UAV协同区域覆盖侦察问题,方法...     

决策树知识表示的多故障源搜索算法研究

在深入研究产生式规则知识表示的基础上,运用决策树知识表示方法,完成故障诊断专家系统的知识库设计,对传统的单故障源决策树搜索算法进行了改进,解决了相同故障现象的多故障源连续搜索和定位问题,提高了推理机单次故障诊断的定位率和搜索效率,缩短了故障诊断专家系统的诊断时间。