基于改进LMS算法的复合材料超声检测缺陷识别

在径向基函数（RBF）神经网络实现无人机复合材料超声检测脱粘缺陷识别时,针对最小均方（LMS）算法在确定网络输出权值时存在稳态失调误差和收敛速度相矛盾的问题,提出一种改进的自适应的变步长LMS算法．该算法根据反馈误差自适应确定步长,通过引进动量项加快收敛速度．将改进LMS算法应用到RBF网络缺陷识别中,结果表明该方法在稳态失调误差较小的情况下,能快速确定RBF网络的权值．改进的RBF网络能够较好地识别超声检测脱粘缺陷．     

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。     

基于效果的炮兵行动C2系统动力学建模与仿真

一体化联合作战参战军兵种多、指挥控制(C2)与协调复杂。围绕炮兵行动特点要求,运用系统动力学理论建立联合作战炮兵行动指挥控制的系统动力学模型,研究常规打击和基于效果作战两种指挥控制模式下炮兵行动所产生的不同结果及其原因,并给合作战想定仿真验证,得出较为可靠的结论,为解决联合作战炮兵行动精确指挥控制与协调提供一种有效途径     

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     

The view from above: how the Royal Air Force provided a strategic vision for operational intelligence during the Malayan Emergency

It has long been held that the Federation of Malaya’s counter-insurgency campaign during the First Malayan Emergency (1948–60) was determined by the use of intelligence. Special Branch — the Federation’s primary intelligence agency — dominates the prevailing paradigm of how the insurgent threat was tackled. Conversely, the role of the Royal Air Force (RAF) within this paradigm is very limited. Most observers simply dismiss the role of photoreconnaissance or airstrikes as being largely inconsequential to the counter-insurgency effort. This is perhaps understandable: the Emergency was after all a ‘policing action’ and the insurgents were largely hidden under Malaya’s jungle canopy and amongst the Chinese community. However, further scrutiny reveals that the RAF made a much more significant contribution to the intelligence element of the counter-insurgency campaign than previously realised. First, the RAF decided to locate their Advanced Headquarters with the Army’s General Headquarters. This led to the creation of the Land/Air Operations Room, through which intelligence, tasking and resources were coordinated. Moreover, the RAF put its intelligence teams into the field to provide a practical link between local units and theatre-level assets. Second, with the support of the Army, the RAF established at the beginning of the Emergency the Joint Air Photographic Intelligence Board (Far East). This coordinated all photographic intelligence requirements throughout the Emergency, which was then delivered via the Joint Air Photographic Centre (Far East). Hence, via Joint Operations Centre and JAPIB (FE), the RAF provided both the practical means for effective joint intelligence operations at theatre level throughout the Emergency.     

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.