自行火炮作战效能分析与模糊综合评价

自行火炮以其机动性强,火力反应快,防护性能好,成为炮兵的重要组成部分和机动作战的骨干力量,但由于自身的复杂性和作战应用的多样性,其作战效能受多方面的因素影响,运用层次分析法确定影响作战效能的指标权重系数,结合模糊综合评价方法将子效能合并成整体体系效能,由此实现自行火炮作战效能的定量化评估。     

预警探测体系作战效能评估框架

首先阐述了预警探测体系及其效能评估的概念,根据预警探测体系及其作战任务的特点,建立了效能评价指标体系,并给出了部分效能指标的评估计算模型,最后提出了预警探测体系作战效能评估的框架和一般过程.     

模糊合成算子在军队指挥效能评估中的应用

随着指挥自动化系统在军事上的广泛应用,在各种效能评估分析中基于模糊综合评判方法也成为一种非常重要的方法.但在实际应用时,很多情况都随机选取模糊合成算子,忽视了模糊合成算子对评估效果影响.结合模糊综合评判在军队指挥效能评估中的应用实例,分析了模糊合成算子对评判结果的影响,并确定了乘与有界算子M(·,(⊙))为适合评估指挥效能的优化算子.     

Optimal capacity in a coordinated supply chain

We consider a supply chain in which a retailer faces a stochastic demand, incurs backorder and inventory holding costs and uses a periodic review system to place orders from a manufacturer. The manufacturer must fill the entire order. The manufacturer incurs costs of overtime and undertime if the order deviates from the planned production capacity. We determine the optimal capacity for the manufacturer in case there is no coordination with the retailer as well as in case there is full coordination with the retailer. When there is no coordination the optimal capacity for the manufacturer is found by solving a newsvendor problem. When there is coordination, we present a dynamic programming formulation and establish that the optimal ordering policy for the retailer is characterized by two parameters. The optimal coordinated capacity for the manufacturer can then be obtained by solving a nonlinear programming problem. We present an efficient exact algorithm and a heuristic algorithm for computing the manufacturer's capacity. We discuss the impact of coordination on the supply chain cost as well as on the manufacturer's capacity. We also identify the situations in which coordination is most beneficial. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

火箭弹防静电短路卡抗静电性能实验研究

通过实验研究了火箭弹系统在安装防静电短路卡前后静态电阻的变化 ,提出了利用脉冲电流发生器测定安装防静电短路卡后系统动态电阻的方法。研究表明 ,安装防静电短路卡后 ,系统动态电阻很小 (小于30mΩ) ,火箭弹抗静电性能明显提高     

行星变速箱换档过程动态仿真研究

从传动系行星排传递单元和构件出发,建立了N自由度行星变速箱运动学数学模型,并运用该模型对三自由度行星变速箱进行了换档过程的动态仿真。     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

基于模糊聚类的多目标跟踪算法

提出了一种新的基于模糊聚类的多目标跟踪算法,该算法通过一种改进的模糊聚类算法,首先得到可能的目标数和测量点迹与目标预测位置之间的隶属度,然后结合Kalman滤波将隶属度作为权值系数对预测新息向量进行加权,来实现目标状态估计的更新。仿真结果表明,传统数据融合多目标跟踪算法,一般需要假定目标数并且在多目标密集时易产生关联错误而导致跟踪发散,新算法通过模糊聚类客观有效地确定了目标数并且通过加权过程保证了对多目标密集时的高精度。     

基于蚁群算法的图像边缘检测研究

将蚁群算法应用于图像分割领域,提出了一种新的基于蚁群算法的图像边缘检测方法。详细阐述了蚁群算法与该方法的基本原理和具体实现过程。为了提高算法效率,进行两处改进,第一将蚂蚁初始位置由随机放置修改为放置在图像边缘附近,可取一图像灰度梯度阈值来实现,第二将信息激素强度和启发式引导函数值均定义为像素点灰度梯度值的函数。大量实验结果证明了该算法能有效地检测出图像边缘,而且具有适应性强、效率高等特点。     

自适应神经模糊推理系统(ANFIS)及其仿真

自适应神经网络模糊推理系统ANF IS是模糊控制与神经网络控制结合的产物。讨论了ANF IS的结构及其特点,并利用M ATLAB的专用工具箱进行了仿真研究,取得满意的效果。