遗传算法及其在导弹火力分配上的应用

遗传算法是一种近年来新发展起来的优化算法,目前它已被广泛应用于解决许多实际问题,如函数优化、图像识别、机器学习、人工神经网络、人工生命、优化调度等许多领域。通过对一种遗传算法的研究,阐述其在导弹武器火力分配上的应用。首先设计和实现了一种遗传算法;然后描述了导弹火力分配优化问题,建立了其数学模型;最后运用遗传算法求解了该问题。     

Variables inspection for SPC‐quarantined production

When a control chart signals an out‐of‐control condition for a production process, it may be desirable to “quarantine” all units produced since the last in‐control SPC sample. This paper presents an efficient procedure for variables inspection of such “SPC quarantined” product. A Bayesian sequential inspection procedure is developed which determines whether the out of control production is of acceptable quality. By inspecting the units in reverse of the order in which they were produced, the procedure is also capable of detecting the point at which the process went out of control, thus eliminating the need to inspect units produced prior to the onset of the out of control condition. Numerical examples are presented, and the performance characteristics of the procedure are demonstrated using Monte Carlo simulation. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 159–171, 2001     

Extending the MAD portfolio optimization model to incorporate downside risk aversion

A mathematical model of portfolio optimization is usually represented as a bicriteria optimization problem where a reasonable tradeoff between expected rate of return and risk is sought. In a classical Markowitz model, the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was developed by Konno and Yamazaki, where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is easily transformed into a linear programming problem. An extension to the MAD model proposed in this paper allows us to measure risk using downside deviations, with the ability to penalize larger downside deviations. Hence, it provides for better modeling of risk averse preferences. The resulting m‐MAD model generates efficient solutions with respect to second degree stochastic dominance, while at the same time preserving the simplicity and linearity of the original MAD model. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 185–200, 2001     

基于专家系统的消磁电流决策

分析了建立消磁电流决策的专家系统的可行性 ,从基本概念和实际应用等方面介绍了基于专家系统的消磁电流决策的状态描述、数据库的建立以及推理机的确立等     

城市环境下单无人机测向定位航迹优化算法

为解决单架无人机在城市环境中对辐射源目标的定位问题,提出了一种基于环境预测法的单无人机测向定位航迹优化算法。使用交互多模型-扩展卡尔曼滤波进行视距和非视距信号混合环境下的目标估计。结合估计的目标位置和城市地理信息模型,基于视线追踪法求解信号遮挡区域和多径信号干扰区域。在滚动时域控制算法框架下生成无人机预测轨迹,以最大化Fisher信息矩阵行列式为测向定位评价准则,考虑建筑物障碍以及其对信号的遮挡和反射效应对无人机测向定位航迹的影响,控制无人机选择最优航向飞行。仿真结果表明,该方法能够使无人机在存在障碍、信号遮挡和多径干扰的环境下实现对目标的高精度测向定位,为解决城市环境下的单架无人机测向定位问题提供了新思路。     

模糊综合评判在指挥信息优势评估中的应用

如何准确地对军队作战行动中获得信息优势的潜在能力进行综合考查和分析具有重要意义,为更好地指导我军信息化建设,分析了现行指挥信息优势评估中的方法的不足.在现有评估方法基础上,基于模糊综合评判,提出了一种更优的隶属度函数来建立模糊评判矩阵中的元素值,并论证确定该隶属度函数的合理性,给出用该隶属度函数确定的模糊评判矩阵的一个实例.用提出的模糊综合评判方法来评估指挥信息优势评估指标体系,在一定程度上提高了对指挥信息优势评估的合理性.     

弹炮结合武器系统作战效能评估

弹炮结合系统是一种有效的防空武器系统,但以往对该武器系统的作战效能评估存在两方面不足:一是较少考虑攻防双方的对抗以及环境对作战效能的影响,使得出的指标可信度降低;二是不同国度、类别、型号的武器系统没有构建统一的价值可比空间,使获得的指标缺乏横向可比性.为此利用灰色关联综合优化法有关原理,给出弹炮结合防空系统作战效能的评价指标体系,确立了相关的隶属函数,进而通过六种弹炮结合防空系统作战效能的优劣排序,验证了该方法有效可行,初步解决了上述问题.     

利用验前信息的一种序贯检验方法——序贯验后加权检验方法

序贯验后加权检验方法是Bayes 统计观点在假设检验中的运用。本文提出了该方法的一般理论,给出了当总体的分布参数具有验前信息时的序贯检验方法。文中确定了决策区的划分,同时讨论了序贯截尾方案,给出了检验中可能犯两类错误的概率的上界,并将一般理论应用于产品的可靠性检验和再入飞行器随机落点的精度鉴定。由于运用了验前信息,因此能有效地在少量试验之下进行统计假设检验。本文所提供的方法对于昂贵产品试验结果的统计评定具有普遍意义。     

尾流自导鱼雷射击弹道优化模型

为实现尾流自导鱼雷整个弹道过程的优化,采用几何分析法。首先分析了尾流自导鱼雷射击弹道需满足的约束条件,然后建立了尾流自导鱼雷一次转角射击和二次转角射击两种射击模式下的射击诸元解算模型,最后建立了尾流自导鱼雷射击弹道优化解算模型,并给出了具体的解算流程图。该模型基于尾流自导鱼雷射击的数学原理,兼顾工程实现时的弹道约束,也充分考虑了其作战使用时的实现流程,能够为尾流自导鱼雷有关问题深入研究提供理论支撑。     

Testing or fault-finding for reliability growth: A missile destructive-test example

A new piece of equipment has been purchased in a lot of size m. Some of the items can be used in destructive testing before the item is put into use. Testing uncovers faults which can be removed from the remaining pieces of equipment in the lot. If t < m pieces of equipment are tested, then those that remain, m₁ = m − t, have reduced fault incidence and are more reliable than initially, but m₁ may be too small to be useful, or than is desirable. In this paper models are studied to address this question: given the lot size m, how to optimize by choice of t the effectiveness of the pieces of equipment remaining after the test. The models used are simplistic and illustrative; they can be straightforwardly improved. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 623–637, 1997