基于QPSO算法的支持向量机参数优化研究

应用具有量子行为的粒子群优化算法,对支持向量（SVM）进行参数优化研究．根据支持向量机的分类准确率和泛化能力之间的关系,应用QPSO算法选取比较优秀的参数模型,比较参数模型的各项性能,选取最适合实际需要的参数模型．仿真表明,QPSO算法的SVM模型与PSO算法相比在分类准确率和泛化能力上均获得更好的效果,经QPSO优化后的SVM整体性能明显提高．     

基于状态变换的数字电路冗余确认研究

以状态跳变图为基础,深入分析了冗余变换与非法变换的特征,提出结构冗余和功能冗余的概念,并讨论了可测故障、不可测故障和冗余之间的联系.最后结合验证和测试生成,提出状态冗余的隐含遍历确认策略.     

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.     

软件定义的航天器测试网络技术

航天器测试网络是航天器自动化测试运行、测试数据监测分析等测试业务运行的信息通道,它的性能将极大影响航天器试验测试效率与试验数据的应用效能。针对传统航天器测试网络存在的物理独立部署、人工迁移、存在数据孤岛以及无法高效交换共享的不足,本文参考借鉴软件定义网络技术,设计了基于虚拟局域网及软件定义网络技术的软件定义航天器测试网络。该测试网络技术具备良好的软件重构、区域隔离、测试信息汇聚共享性能,能够通过软件重构,自动化迁移部署航天器测试网络;通过网络区域逻辑隔离,确保各航天器测试指令运行区域受控、安全;通过全局测试数据汇聚实现多航天器测试数据的交换共享,从而有效解决了航天器测试网络快速部署、测试指令安全运行与测试数据交换共享的问题,有力推动了装备试验数据工程的建设工作。     

基于虚拟目标的反舰导弹的最优制导律研究

为了对抗敌方预警系统的跟踪、预测以及远程防空导弹的拦截,反舰导弹的飞行弹道应采用大空域变轨弹道。将弹目相对运动分解为俯仰和航向两个平面内的运动,考虑了制导过程中能量消耗最小以及末端角度要求,分别设计了两个平面内的最优制导律。利用该制导律可以引导反舰导弹向大空域内任何虚拟目标进行变轨机动,从而实现了各种各样的大空域变轨弹道。只要虚拟目标参数设置合适,反舰导弹的各项性能指标均能满足要求。弹道仿真表明了所设计的最优制导律的有效性。     

机床颤振中“双频颤振”的研究

文中首次提出了机床颤振中“双频颤振”的论点,并对其产生机理进行了理论探讨且得实验验证。作者认为:对机床颤振中“双频颤振”进行研究不仅具有很重要的实用价值,而且将导致对现行的机床颤振理论的某些修改。     

基于Virtual．Lab的某型减速器建模及仿真

为了预测某型自行火炮减速器的寿命,基于Pro/E实体模型,用Virtual.Lab建立了其虚拟样机模型,提出并简述了动态关联的模型校核验证方法,并利用动态关联法,对所建立的虚拟样机模型进行了验证,确认所建立的减速器虚拟样机符合实际情况,同时该虚拟样机模型通过加载得到了二档速度B级路面下太阳齿轮受到的扭矩,为下一步齿轮的校核打下了基础。     

多转子-轴承系统虚拟样机建模及仿真

以LMS Virtual.Lab Motion为开发环境建立了某传动箱多转子-轴承系统虚拟样机,导入PRO/E模型文件进行简化,建立实体模型。针对目前所建立的齿轮传动系统虚拟样机过于简化的情况,建立了考虑时变啮合刚度的非线性扭转振动模型。通过组合副和用户自定义程序,实现了齿轮副和轴承端的耦合振动模型。仿真结果表明:通过虚拟样机仿真的方法获得载荷谱是可行的,其可用于深入研究传动箱的振动分析、强度校核和寿命预测。     

基因遗传算法在三维数据场造型中的应用

将基因遗传算法应用于三维数据场的造型研究之中,提出了遗传三角剖分算法.针对三维三角剖分的特殊性,提出了虚拟交叉算子和三角变异算子,能够确保在遗传进化过程中,解群中的每一个串始终代表一个合法的三角剖分.     

几类基本学习认知模型

本文给出了几个类型的学习认知模型,并加以讨论