谈网络理论与现代控制论的交叉结合

当前计算机技术的飞速发展和多学科的交叉结合,给复杂网络的设计和计算等带来了深刻的变化。"现代控制论"与"现代网络理论"一直紧密地联系着,控制论的要领与方法已渗透到网络分析中,可以认为网络理论是受基尔霍夫定律约束的系统理论的子学科。     

神经网络能力研究

文献[1]～[3]中研究了神经网络的能力—存储能力。但我们认为神经网络的能力应包括存储能力和计算能力两个方面。本文对神经网络的存储能力和计算能力进行定量分析,并得出了许多有益的结论。     

基于动态贝叶斯网络的复杂网络抗毁性分析

复杂网络抗毁性是复杂网络在节点或边遇敌攻击后能继续维持基本功能的能力,是衡量军事信息网络鲁棒性和敏捷性的重要指标。针对复杂网络及对作战体系支撑能力的多指标、复杂化和动态演绎特点,在静态分析方法基础上,提出基于动态贝叶斯网络的抗毁性分析方法。建立了复杂网络抗毁性指标体系。构建了基于动态贝叶斯网络的复杂网络抗毁性评估模型,提出确定评估模型参数的方法。仿真验证了方法的可行性和有效性。     

Research on construction of operation architecture based on complex network

Operation architecture plays a more important role in Network Centric Warfare (NCW), which involves dynamic operation networks with complex properties. Thus, it is essential to investigate the operation architecture under the informatization condition within NCW and find a proper network construction method to efficiently coordinate various functional modules on a particular situation, i.e., the aerial combat. A new method integrating the physical level and functional level of NCW is proposed to establish the operation architecture, where the concept of network operation constraints unit and net constructing mechanisms are employed to avoid conflicts among different platforms. Meanwhile, we conduct simulations to assess the effectiveness and feasibility of the constructed operation architecture and analyze the influence of the network parameters.     

污水处理过程软测量系统辅助变量的选择方法

辅助变量的选择包括变量类型、数量及检测点的选择,是软测量建模的第一步,直接关系到软测量质量的好坏.针对活性污泥法,从微生物生长繁殖的角度出发,探讨了各要素对于微生物生长活动能力的影响及其相互关系,通过影响力大小及相互间耦合性的复杂程度选取辅助变量的原则,完成了污水处理辅助变量类型的初选,并对辅助变量的精选进行了讨论.     

装备保障综合演练风险分析

采用工作结构分解(Work Breakdown Structure,WBS)和风险结构分解法(Risk Breakdown Structure,RBS)对装备保障综合演练全过程的风险及其风险因素进行了系统分析,得出了装备保障综合演练过程中存在的7类风险、24种风险因素;运用网络层次分析法(Analytic Network Process,ANP)对各种风险及其风险因素进行了评估,得出了其对装备保障综合演练风险影响的权重及排序。研究结果可为装备保障综合演练顺利开展,确保人员、装备安全提供参考。     

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.     

网络中心作战体系效能评估方法初探

网络中心作战体系是信息化战争要求下的一个具体作战体系,其效能评估是网络中心战研究领域的重要问题,是解决网络中心作战体系建设中方案论证、性能效能指标、经济因素等诸多问题的有效方法。首先提出了网络中心作战体系效能评估的基本概念;然后阐述了网络中心作战体系效能评估方法问题,分析介绍了几种较为成熟的评估方法的特点、适用性和不足;最后提出一种基于探索性建模与分析方法的网络中心作战体系效能评估框架,作为研究网络中心作战体系效能评估的参考评估框架。     

External security determinants of Greek military expenditure: An empirical investigation using Neural networks

Greece has regularly ranked as the country with the highest defence burden in NATO and the European Union. Over the past decades she has allocated an averatge 6% of GDP to defence yearly. This study using neural networks examines the external security determinants of Greek military expenditure in the context of the ongoing Greek‐Turkish conflict.     

How Probable is Cyber War? Bringing IR Theory Back In to the Cyber Conflict Debate

Many well-established explanations for war suggest that cyber weapons have a greater chance of being used offensively than other kinds of military technologies. This response article introduces a research agenda for the study of cyber war, and offers an example – principal-agent problems in cyber operations – to demonstrate how rigorous theoretical and empirical work may proceed.