What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

多操纵面战斗机飞行控制系统设计

现代多操纵面战斗机飞行控制系统多采用非线性控制方法进行设计,其中逆系统方法具有物理概念清晰、参数对应明确、无不确定性算法、容易结合传统经验等优点,易于工程实现.将该方法应用于多操纵面战斗机的基本控制律设计,使用多层简化设计,改进了多操纵面的控制分配策略,并针对某型多操纵面战斗机六自由度模型进行数字仿真,效果良好.     

基于灰色非线性回归模型的故障预测

为克服传统灰色模型的局限性,通过对一阶累加生成序列规律性的分析,将灰色模型和非线性回归模型相结合,构造了一种灰色非线性回归模型。实例仿真结果表明,该模型既拓展了传统灰色模型的适用条件,又比传统灰色模型和非线性回归模型具有更高的预测精度,且适用性广。     

基于高斯和-容积Kalman滤波的高轨双星辐射源跟踪

高轨双星系统可通过测量辐射源信号的到达时差、频差和到达高轨卫星的多普勒频移,跟踪沿地球表面巡航且载频固定、已知的运动辐射源。针对辐射源运动方程和观测方程的强非线性,提出了基于高斯和框架与5阶容积Kalman滤波的运动辐射源跟踪算法GS-5CKF。算法将起始时刻的时差观测量所确定的处于地球表面的时差线按辐射源经度等间隔划分,初始化多个并行的5阶容积Kalman滤波器,线性组合各滤波器每个时刻的输出以获得辐射源运动状态的估计。针对5阶容积Kalman滤波器,提出了相应的非线性测度并引入滤波器分裂与合并,以提高跟踪精度并保持GS-5CKF算法计算复杂度基本不变。仿真表明,相对仅使用单个5阶容积Kalman滤波器和基于高斯和框架但使用3阶容积Kalman滤波器的GS-3CKF等算法,提出的跟踪算法具有更高的估计精度。     

Making defense planning meaningful: the evolution of strategic guidance in the Hungarian Ministry of Defense

Hungary, a former communist state, adapted a Western-style defense planning system during the 1990s and 2000s. Although on the surface the elements of this planning system were similar to the planning programming budgeting system (PPBS) developed by the US Department of Defense, strategic guidance for defense planning has not been properly developed until recently. Thus, albeit PPBS-based defense plans were developed in the Hungarian Ministry of Defense (Hungarian MoD) regularly, they lacked both an expression of clear priorities and strategic focus. This article delineates the evolution of strategic guidance in the Hungarian MoD concentrating on current developments, and introduces the newly elaborated analytical concepts and tools, which helped to create needed strategic guidance in Hungary.     

基于协方差矩阵估计的稳健Capon波束形成算法

常规Capon波束形成器性能对模型误差或失配非常敏感,尤其是当期望信号包含在训练数据中,导向矢量失配将引起性能急剧下降。为解决这一问题,提出了一种采用干扰噪声协方差矩阵和导向矢量联合估计的稳健波束形成算法。该方法通过对Capon空间谱在非目标信号的方位区域内的积分,实现对干扰噪声协方差矩阵的估计,解决数据协方差矩阵包含有目标信号时引起信号自相消问题;其次为了克服导向矢量失配的影响,通过最大化输出功率,并增加二次型约束防止估计的导向矢量接近于干扰导向矢量,实现对导向矢量的估计。仿真实验表明:该算法能获得近似最优的输出信干噪比,与现有算法相比稳健性更强。     

Expansion planning for waste‐to‐energy systems using waste forecast prediction sets

We consider an expansion planning problem for Waste‐to‐Energy (WtE) systems facing uncertainty in future waste supplies. The WtE expansion plans are regarded as strategic, long term decisions, while the waste distribution and treatment are medium to short term operational decisions which can adapt to the actual waste collected. We propose a prediction set uncertainty model which integrates a set of waste generation forecasts and is constructed based on user‐specified levels of forecasting errors. Next, we use the prediction sets for WtE expansion scenario analysis. More specifically, for a given WtE expansion plan, the guaranteed net present value (NPV) is evaluated by computing an extreme value forecast trajectory of future waste generation from the prediction set that minimizes the maximum NPV of the WtE project. This problem is essentially a multiple stage min‐max dynamic optimization problem. By exploiting the structure of the WtE problem, we show this is equivalent to a simpler min‐max optimization problem, which can be further transformed into a single mixed‐integer linear program. Furthermore, we extend the model to optimize the guaranteed NPV by searching over the set of all feasible expansion scenarios, and show that this can be solved by an exact cutting plane approach. We also propose a heuristic based on a constant proportion distribution rule for the WtE expansion optimization model, which reduces the problem into a moderate size mixed‐integer program. Finally, our computational studies demonstrate that our proposed expansion model solutions are very stable and competitive in performance compared to scenario tree approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 47–70, 2016     

Nonlinear pricing with consumer satiation

In various scenarios, consumers may become satiated with products, and the degree of satiation is directly associated with their prior experiences. Confronted with consumer satiation, the seller is unable to either identify consumers who have a higher likelihood of being satiated ex ante or distinguish satiated from non‐satiated consumers ex post. Therefore, the seller should address dynamic selling, valuation uncertainty, and quantity decisions, all of which are important operational issues. We consider a two‐period problem in which consumer types are influenced by their prior consumption experiences. Faced with these consumers, the seller intends to optimize quantities and adjust the prices of the products in each period to maximize revenue. We find that the seller may reduce ex ante production quantity as some consumers become satiated. Moreover, the ex ante quantity is first decreasing and then increasing with regard to the satiation rate. Furthermore, two‐period information asymmetries may provide a rationale for upward distortion in quantity when consumer preferences are highly sensitive to first‐period consumption. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 386–400, 2016     

配套装备器材的库存与运输集成优化研究

针对我军配套装备器材订货的特点,在考虑库存容量空间限制与整套装备的最低期望满足率两种约束条件下,建立了配套装备器材的库存与运输优化模型,并应用改进的动态规划方法进行求解。结果表明：应用库存与运输的优化模型,在保障军事目标实现的前提下,有效地降低了物流成本。     

非线性系统的神经网络广义预测控制

研究了神经网络广义预测控制方法在非线性系统中的应用,基于BP网络构造神经网络预测器,利用非线性系统的开环输入输出数据离线训练神经网络,根据拟牛顿BFGS优化算法使得二次型性能指标函数达到最小,得到了最优的控制序列。同时给出了神经网络广义预测控制算法的步骤,讨论了提高系统鲁棒性的措施。仿真结果表明,这种神经网络预测控制算法具有响应速度快、控制效果好和跟踪精度高等特点。