基于复杂网络的作战体系网络模型分析

运用复杂网络方法,构建了作战体系网络模型,分析了作战体系网络的基本特征,揭示了作战网络主要参数与作战体系真实特性之间的关系,得到了基于信息系统体系作战战法运用的一些启示。     

战术数据链网络同步技术的一种改进算法

现有的Link16网络同步算法,假定了询问与应答报文的传播时间相同,导致E值误差较大。提出一种改进算法,待同步JU相继向网内不同NTR发送询问报文,NTR分别发回应答报文。同时NTR间也发送往返计时报文,通过联立计算,提高时间误差的精度。改进算法的仿真分析结果,验证了新同步算法的可行性和有效性。     

LEACH—DRT算法中基于神经网络的数据预测算法

为了保证无线传感器网络中数据的完整性,针对基于LEACH路由协议的动态轮时间算法存在的问题,提出一种基于人工神经网络的数据预测算法。该动态轮时间算法中,部分簇因调整后的轮时间不足以完成数据的采集而丢失数据。数据预测算法结合传感器节点数据具有时空相关性的特点,将时空延迟算子引入神经网络模型,并通过建立的神经网络模型对数据进行预测。仿真时采用伯克利英特尔实验室的传感器数据,通过Mafl软件对模型进行测试并分析仿真结果。实验结果表明：该算法对连续多个数据的预测效果理想,预测误差始终保持在较低水平。     

A three‐stage procurement optimization problem under uncertainty

This article examines a problem faced by a firm procuring a material input or good from a set of suppliers. The cost to procure the material from any given supplier is concave in the amount ordered from the supplier, up to a supplier‐specific capacity limit. This NP‐hard problem is further complicated by the observation that capacities are often uncertain in practice, due for instance to production shortages at the suppliers, or competition from other firms. We accommodate this uncertainty in a worst‐case (robust) fashion by modeling an adversarial entity (which we call the “follower”) with a limited procurement budget. The follower reduces supplier capacity to maximize the minimum cost required for our firm to procure its required goods. To guard against uncertainty, the firm can “protect” any supplier at a cost (e.g., by signing a contract with the supplier that guarantees supply availability, or investing in machine upgrades that guarantee the supplier's ability to produce goods at a desired level), ensuring that the anticipated capacity of that supplier will indeed be available. The problem we consider is thus a three‐stage game in which the firm first chooses which suppliers' capacities to protect, the follower acts next to reduce capacity from unprotected suppliers, and the firm then satisfies its demand using the remaining capacity. We formulate a three‐stage mixed‐integer program that is well‐suited to decomposition techniques and develop an effective cutting‐plane algorithm for its solution. The corresponding algorithmic approach solves a sequence of scaled and relaxed problem instances, which enables solving problems having much larger data values when compared to standard techniques. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

New results on rendezvous search on the interval

In a rendezvous search problem, two players are placed in a network and must try to meet each other in the least possible expected time. We look at rendezvous search on a discrete interval in which the players are initially placed using independent draws (usually assumed to be from the same distribution). Some optimal solutions are known if this distribution is uniform, and also for certain other special types of distribution. In this article, we present two new results. First, we characterize the complete set of solutions for the uniform case, showing that all optimal strategies must have two specific properties (namely, of being swept and strictly geodesic). Second, we relate search strategies on the interval to proper binary trees, and use this correspondence to derive a recurrence relation for solutions to the symmetric rendezvous problem for any initial distribution. This relation allows us to solve any such problem computationally by dynamic programming. Finally, some ideas for future research are discussed. © Wiley Periodicals, Inc. Naval Research Logistics 60: 454–467, 2013     

遗传神经网络在保障资源需求预测中的应用

针对现代战争条件下装备保障资源需求变化快,保障资源预测困难的问题,首先分析了影响装备保障资源需求的因素,根据实际情况选取了平均维修间隔时间(MTBM)、平均修复时间(MTBR)等8项影响装备保障资源需求的关键指标,然后将基于遗传算法(GA)优化的反向传播(BP)神经网络应用于保障资源需求预测中,构建了基于遗传神经网络的需求预测模型,最后利用1980年～2010年实际保障资源需求数据对模型进行了验证.验证结果表明,基于GA优化的BP神经网络预测模型有较快的收敛速度、较强的适应性和较高的预测精度,适用于装备保障资源需求预测.     

改进的粒子群神经网络在飞控中的应用

针对BP神经网络对初始值敏感、容易陷入局部寻优且收敛速度较慢,提出用粒子群对神经网络的参数进行优化,同时设计了衰减的指数函数对惯性权重进行动态调整以提高算法性能.并应用该算法对导弹飞控系统的逆误差进行补偿,仿真结果表明,该方法对逆误差进行了有效的补偿,避免了局部寻优并提高了学习效率.     

火炮发射药装药自动识别方法

发射装药号是影响火炮身管寿命的重要因素,为自动识别火炮发射药的装药号,避免人工记录失误和完善火炮射弹履历,分析了装药号自动识别原理,运用火炮动力学分析理论和ADAMS虚拟样机技术,建立火炮发射动力学虚拟样机.通过仿真试验获取样本数据,应用BP神经网络进行学习和训练,从而建立装药号和测试数据之间的非线性映射关系,实现对火炮发射装药号的精确预测.     

基于网络演算理论计算车电系统总线的最大时延

以车电系统总线为研究对象,以网络演算理论为数学工具,提出一种基于网络演算理论的车电系统总线的最大时延计算模型.通过仿真实验得到了车电系统总线中各个信息帧的到达曲线、高优先级的信息帧集合的到达曲线、服务曲线和最大时延的数值结果.与相关研究相比,所建模型输入参数少,计算量小,可以快速求解车电系统总线的最大时延,具有良好的适应性和通用性,可以作为车电系统总线分析、设计与优化的理论支持工具.     

网络多媒体技术结合案例教学法在《心理健康教育》教学中的应用

科学信息的快速发展给高校心理健康教育提出了新的挑战,网络多媒体相结合案例教学的教育模式已成为高校教育模式发展的趋势。文章阐述了在《心理健康教育》教学中运用网络多媒体技术结合案例教学的心得体会,分析了网络多媒体结合案例教学的优势。