复杂网络下基于Agent的舆情演化研究

<正>确认识、预测、引导和处理舆情对公众的影响,对社会稳定有重大的现实意义。通过综合运用多Agent和动力学微分方程的思路建立模型,结合社会人群中个体的行为规律,重点描述个体的行为细节和交互规则,构建了自下而上的舆情演化模型,并模拟广场"自焚"事件在网络上的传播,对模型进行了实证研究,验证了模型的有效性,运用Net Logo软件进行数值仿真,得到了舆情演化的规律,进而提出相应的对策建议。     

战场QPSK通信系统干扰注入仿真研究

无线射频干扰注入可对无线通信网络系统进行干扰,甚至实现非授权接入实施网电攻击。若捕获敌方的通信协议,掌握其采用的通信模式,如何进行干扰甚至将虚假的信息注入无线网络,是实施网电攻击的技术难点。针对指挥通信系统中的无线开放环节,基于软件无线电技术,分析了战场通信目标信号系统的技术指标,对编码方式、信号带宽、载波频率、调制方式和调制滚降系数等主要参数进行分析,在MATLAB/Simulink环境下建立QPSK数字频带传输系统和射频干扰注入系统模型,并且对关键节点进行仿真。仿真结果表明,在不同干扰方式下,通信系统会受到较明显的干扰,或者解调出与注入一致的干扰信息,达到了验证设计的目的。     

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.     

Adaptive control of track tension estimation using radial basis function neural network

Track tension is a major factor influencing the reliability of a track. In order to reduce the risk of track peel-off, it is necessary to keep track tension constant. However, it is difficult to measure the dynamic tension during off-road operation. Based on the analysis of the relation and external forces depending on free body diagrams of the idler, idler arm, road wheel and road arm, a theoretical estimation model of track tension is built. Comparing estimation results with multibody dynamics simulation results, the rationality of track tension monitor is validated. By the aid of this monitor, a track tension control system is designed, which includes a self-tuning proportional-integral-derivative (PID) controller based on radial basis function neural network, an electro-hydraulic servo system and an idler arm. The tightness of track can be adjusted by turning the idler arm. Simulation results of the vehicle starting process indicate that the controller can reach different expected tensions quickly and accurately. Compared with a traditional PID controller, the proposed controller has a stronger anti-disturbance ability by amending control pa-rameters online.     

基于神经网络的广义两点边值问题

在航天领域中 ,多数制导问题均可以抽象为广义两点边值问题。本文探讨了神经网络在广义两点边值问题求解中的应用。首先讨论了离线制导这一类静态两点边值问题的求解 ,在此基础上 ,对在线制导这一类动态两点边值问题进行了分析 ,并给出了有效的解决方法     

基于遗传算法的通信网络可靠性优化设计

在可靠性条件约束下 ,使网络成本最低是网络规划NP hard问题 .文章提出一种基于遗传算法的优化方法解决了这类问题 .仿真结果表明这种算法是有效的 .     

多重结构神经网络在平台罗经故障诊断中的应用

提出了基于层次分类诊断模型的多重结构神经网络 (MNN)在平台罗经故障诊断中的应用方法 ,并对其温控子系统的人工神经网络 (ANN)进行了Matlab实现     

基于WAN环境的物资信息管理系统的规划与设计

针对物资保障的信息化建设和物资保障网络管理信息系统的开发,论述了系统应具备的特点,提出了系统建设的总体方案.对信息系统功能进行了总体规划和划分对信息系统建设的关键技术进行了讨论.最后,结合信息系统开发方法学对系统开发的具体方法进行了讨论.     

神经网络在目标属性识别中的应用研究

首先介绍了目标属性识别的方法,并对多属性决策模型(MultipleAttributeDecisionModel,MADM)目标属性识别模型进行了分析,在此基础上提出了基于神经网络的目标属性识别模型,其能有效地解决目标属性识别问题。     

神经网络技术在冲压导弹设计中的应用

利用近年来迅猛发展的神经网络技术,成功地建立了某型冲压导弹气动参数和发动机推力、比冲的数学模型,从而克服了用传统回归拟合方法和数学插值计算的不足,为导弹总体参数优化设计和各分系统设计提供了一种新工具.并对网络训练过程中存在的问题进行了分析,提出了具有实际意义的改进方法.