自适应二阶滑模制导律

传统基于Lyapunov定理所设计的制导律无法保证制导系统的有限时间收敛.基于滑模控制理论,给出了一种二阶滑模有限时间收敛方法,并将该方法与机动目标拦截情形相结合,进行制导律的设计.该制导律首项使得视线转率趋于0,而扩展项可满足视线成型,滑模面的有限时间收敛等特性.针对2种典型的目标机动形式进行了仿真研究,结果表明该滑模制导律可以实现目标的有效拦截,满足上述特性.     

无人机对地目标多帧融合定位与误差收敛特性分析

对地目标高精度定位是无人机开展目标侦察、火力引导、效能评估等的重要前提,然而无人机对地目标定位精度受到误差因素多、传递链长等因素的制约。采用基于卡尔曼滤波的图像多帧配准对地面目标定位的方法,通过融合无人机获取的多帧目标图像,基于卡尔曼滤波方法,研究无人机对地目标高精度融合定位方法,并引入蒙特卡洛法进行仿真,分析基于卡尔曼滤波法多帧融合定位的误差收敛性、大小和分布,分析观测间隔、视线俯仰角等对误差收敛性的影响,形成若干提高定位精度的建议。     

随机快速光滑二阶滑模末制导律设计

针对目标随机机动、惯性延迟、参数变化等因素降低导弹末制导精度的问题,提出新型随机快速光滑二阶滑模控制方法。将目标机动简化为零均值高斯白噪声过程,制导系统成为带加性噪声随机不确定非线性系统。考虑到该系统不存在平衡点,提出有限时间二阶均方实用收敛概念,并基于此证明了所设计控制律的收敛特性。根据直接命中条件设计滑模面,得到随机快速光滑二阶滑模制导律。在尾追和迎头两种态势下,将该新型制导律与扩展比例导引、一般滑模制导律及确定性光滑二阶滑模制导律进行仿真比较,验证了该方法的正确性和有效性。     

搜索力最优配置的求解与收敛性分析

讨论了搜索线上搜索力（密度）的最优配置问题,给出了求解的算法。并对其收敛性作了证明。     

利用会聚区隐蔽解算目标运动要素的方法

在浅海,当被动声纳探测到目标后,由于舰艇间不断的运动,测得的目标方位随时间的不同而不断地发生变化,利用目标方位的变化,通过纯方位法和滤波估计等方法可解算出目标的运动要素.当声纳利用深海会聚区的声学效应在远距离上探测到目标时,因舰艇被动声纳在隐蔽条件下往往只能测得目标的方位,此时的目标方位随着时间的不同变化很小,难以利用浅海的解算方法求出目标的运动要素.为解决这一问题,基于对深海会聚区声学效应的分析,提出了利用会聚区宽度等特点,采用本舰艇合理机动的方式,来求解目标的运动要素的方法.通过对本舰艇各种可能机动方式的建模和计算机仿真计算的结果可见,该方法可行,且在第一、第二会聚区效果明显.     

A Bayesian analysis on the effect of multiple supply options in a quick response environment

In the apparel industry, vendors often suffer from high mismatches in supply and demand. To cope with this problem, they procure the same style product from different suppliers with different manufacturing costs. Especially in the quick response environment, which allows vendors to monitor trends in customer demand and search for available suppliers through the electronic market, they have additional opportunities to improve their decision‐making. In this paper, we propose an analytical profit maximization model and develop efficient decision tools to help both the middle and lower level managers pursuing this strategy. Furthermore, we have shown how significantly the vendors' potential competitive edge can be improved by exploiting multiple supply options, even at the expense of high premium procurement costs for late orders. The effect is critical, especially in a highly competitive market, and it has important implications for the top managers. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

关于非扩展映象的有限族的一个强收敛性定理

讨论了一个非扩展映象的有限族所定义的迭代格式,证明了Banach空间中的一个非扩展映象的有限族的强收敛性定理。     

随机变量序列最大值函数几乎处处收敛

设X1,X2,…为独立同分布随机变量序列,具有公共分布函数F,F绝对连续并具有密度函数F′。在一定条件下得出了随机变量序列最大值函数的一类极限分布,并证明了随机变量序列最大值函数几乎处处收敛于其对应极限分布的密度函数之上。     

On the hierarchy of γ‐valid cuts in global optimization

Concavity Cuts play an important role in concave minimization. In Porembski, J Global Optim 15 ( 17 ), 371–404 we extended the concept underlying concavity cuts which led to the development of decomposition cuts. In numerical experiments with pure cutting plane algorithms for concave minimization, decomposition cuts have been shown to be superior to concavity cuts. However, three points remained open. First, how to derive decomposition cuts in the degenerate case. Second, how to ensure dominance of decomposition cuts over concavity cuts. Third, how to ensure the finite convergence of a pure cutting plane algorithm solely by decomposition cuts. These points will be addressed in this paper. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Stochastic production capacity: A bane or a boon for quick response supply chains?

The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold.