非致命微波武器综合效能评估

针对非致命微波武器效能评估问题,通过分析影响非致命微波武器效能的关键因素,建立了非致命微波武器评价指标体系,提出了非致命微波武器效能的评估方法。采用一致性排序法确定各个指标的分配权重,并将Vague集应用到效能评估中,利用加权贴近度表征非致命微波武器效能的评估值。得到评估结果与实际情况相符,为国内非致命微波武器发展提供了理论依据。     

卫星通信网作战效能评估研究

通过对卫星通信网完成信息传输作战任务的能力分析,建立了卫星通信网作战效能评估指标体系;将模糊层次分析法和熵权法相结合,建立了确定指标权重向量的综合集成赋权模型,采用专家打分和隶属度函数法计算单因素模糊评估向量,采用相对状态特征值对评估结果进行处理,建立了模糊综合评估模型,实现了卫星通信网作战效能的评估,并对评估结果进行了分析。     

信息熵与物元理论在某新型火炮技术阵地优选中的应用

针对某新型火炮武器系统的作战特点,运用数学理论探索其技术阵地的优选问题。提出并利用信息熵和物元理论相结合的优选方法,依据该型火炮技术阵地选择的主要因素,建立了该型火炮技术阵地优选的数学模型。并运用该模型,结合具体实例,对该型火炮的预选技术阵地进行了优选。与传统方法相比,该方法不仅计算简便、收敛快,而且考虑技术阵地选择的不确定因素充分,为辅助指挥员实现科学决策提供了参考。     

层次分析法在对潜多目标威胁度评估中的应用

针对潜艇行动隐蔽的主要特点,引入层次分析法来解决潜艇威胁度评估问题。以水面舰艇对潜作战为背景,对影响潜艇威胁度评估的六种属性进行分析,提出基于层次分析法的多目标潜艇威胁度评估模型。通过建立层次结构模型,构造判断矩阵,获取了六种属性的权重值,并进行实例计算演示了评估的过程。该方法可一定程度地解决对潜作战多目标威胁度评估和排序问题,提高水面舰艇对潜火力分配效率。     

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。     

基于主观综合评判的作战仿真可信度评估方法研究

可信度是作战仿真的生命线,对作战仿真系统建设全生命周期进行校核、验证与确认(verification,validation and accreditation,VVA)是确保其可信度的重要途径。合适的评估方法是进行可信度评估的前提条件。在全面分析与比较已有可信度评估方法基础上,针对作战仿真可信度评估中突出主题专家作用的特点,提出了一种基于主观综合评判的可信度评估方法,重点设计了基于专长权的主题专家权重定量计算方法。这些方法都被应用于"××作战仿真系统"的可信度评估工作中。实践表明,本文的研究对于作战仿真系统等复杂仿真系统的可信度评估非常有效。     

非线性滤波方法在水下目标跟踪中的应用

在对扩展卡尔曼滤波、不敏卡尔曼滤波和粒子滤波3种非线性滤波方法进行研究的基础上,对粒子滤波算法的重要性密度函数的选取方法进行了研究。结合水下目标的被动跟踪的应用背景,比较了3种滤波算法在水下目标跟踪中的性能差异。结果表明,粒子滤波算法能较好的用于非线性、非高斯条件下的水下目标跟踪。     

A worst‐case formulation for constrained ranking and selection with input uncertainty

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

熵权灰色关联分析法的坦克夜间射击目标选择

针对夜间战场上坦克目标威胁评估问题,结合多目标决策问题,将熵权和灰色关联分析法相结合,提出一种基于熵权灰色关联分析的坦克夜间射击目标选择模型,并详细给出了该算法的思想、算法.该方法克服了传统评估算法中主观赋值的缺陷,最后利用实例证明了该方法的有效性.