基于FNN和RS理论的综合评估及应用实例

为较好解决武器系统效能综合评价与分析的问题,提出一种新的综合评价与 分析方法。以某一类型的武器装备的效能评估为例,利用模糊神经网络(FNN)的方法和粗 糙集(RS)理论方法对武器装备效能的优劣进行评估。首先,用FNN方法提取用于效能评 估的模糊规则,然后用一种新的处理不确定知识的数学工具,粗糙集理论方法对装备属性进 行约简,删除其中不相关或不重要的知识,选出最重要的且尽可能少的评价指标获得系统效 能评价的最小决策算法,进而分析得到系统效能的关键因素。     

Discrete stochastic optimization using variants of the stochastic ruler method

We present two random search methods for solving discrete stochastic optimization problems. Both of these methods are variants of the stochastic ruler algorithm. They differ from our earlier modification of the stochastic ruler algorithm in that they use different approaches for estimating the optimal solution. Our new methods are guaranteed to converge almost surely to the set of global optimal solutions under mild conditions. We discuss under what conditions these new methods are expected to converge faster than the modified stochastic ruler algorithm. We also discuss how these methods can be used for solving discrete optimization problems when the values of the objective function are estimated using either transient or steady‐state simulation. Finally, we present numerical results that compare the performance of our new methods with that of the modified stochastic ruler algorithm when applied to solve buffer allocation problems. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于模糊多属性决策的威胁度评估与排序研究

应用模糊多属性决策理论与方法,提出一种对多批目标威胁度大小综合评估和排序的方法。此方法用模糊集和模糊数来定义和表述问题,应用模糊多属性决策方法和模糊算法实现威胁度评估并排序,使结果更能逼近于真实的空中态势状态,有利于现代实战环境中实现作战指挥决策的科学化。     

拖航安全性影响因素权值的确定方法

运用重要度排序指数法对拖航安全性影响因素的权重进行评价,对变权综合决策方法在舰船拖带中的应用进行了探讨.针对舰船拖带这一多目标决策问题建立了决策模型,对于影响拖带安全的主要指标的权重给出了相应的计算方法,并通过实例对拖带方案进行了评估,为舰船拖带的最优化评定提供了有效的解决途径.     

目标打击价值的灰色决策分析

目标价值分析是舰艇指控系统进行目标分配和火力通道组织的基础性工作.为了评估目标价值大小,从分析影响目标价值大小的主要因素着手,将其归结为多目标决策中的方案排序问题,用灰色关联分析法建立了战场多目标打击顺序的评估模型.实例计算分析表明,所提出的方法较好地解决了评估中的多指标问题,计算结果与实际经验判断非常吻合,弥补了指挥员主观判断上的某些缺陷,为舰艇指控系统适时进行目标打击价值分析提供了一种有效的参考处理方法.     

数据融合中的Dempster-Shafer证据理论

虽然 D- S方法已广泛地应用于各种数据融合系统中 ,但在实际应用中依然存在着许多困难。总结了数据融合应用中的 Dempster- Shafer证据理论研究概况 ,从四个方面阐述和对比分析了一些有代表性的研究成果 ;同时为下一步的研究提出了新的课题和思路     

Bayesian sampling plans for exponential distributions with interval censored samples

This article studies the problem of designing Bayesian sampling plans (BSP) with interval censored samples. First, an algorithm for deriving the conventional BSP is proposed. The BSP is shown to possess some monotonicity. Based on the BSP and using the property of monotonicity, a new sampling plan modified by the curtailment procedure is proposed. The resulting curtailed Bayesian sampling plan (CBSP) can reduce the duration time of life test experiment, and it is optimal in the sense that its associated Bayes risk is smaller than the Bayes risk of the BSP if the cost of the duration time of life test experiment is considered. A numerical example to compute the Bayes risks of BSP and CBSP and related quantities is given. Also, a Monte Carlo simulation study is performed to illustrate the performance of the CBSP compared with the BSP. The simulation results demonstrate that our proposed CBSP has better performance because it has smaller risk. The CBSP is recommended. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 604–616, 2015     

基于无偏GM(1,1)幂模型的意图威胁建模

在态势威胁的基础上,利用无偏GM(1,1)幂模型设计了一种敌机意图威胁的模型。首先,提出了素质因子和探测概率因子,在此基础上,建立了改进的态势威胁模型。然后,将角度作为特征量对敌机机动进行预测,并建立了意图威胁模型。最后,分别对态势威胁和意图威胁模型进行仿真。结果表明,意图威胁模型行之有效。     

Dynamic policy for idling time preservation

This study aims to determine and evaluate dynamic idling policies where an agent can idle while some customers remain waiting. This type of policies can be employed in situations where the flow of urgent customers does not allow the agent to spend sufficient time on back-office tasks. We model the system as a single-agent exponential queue with abandonment. The objective is to minimize the system's congestion while ensuring a certain proportion of idling time for the agent. Using a Markov decision process approach, we prove that the optimal policy is a threshold policy according to which the agent should idle above (below) a certain threshold on the queue length if the congestion-related performance measure is concave (convex) with respect to the number of customers present. We subsequently obtain the stationary probabilities, performance measures, and idling time duration, expressed using complex integrals. We show how these integrals can be numerically computed and provide simpler expressions for fast-agent and heavy-traffic asymptotic cases. In practice, the most common way to regulate congestion is to control access to the service by rejecting some customers upon arrival. Our analysis reveals that idling policies allow high levels of idling probability that such rejection policies cannot reach. Furthermore, the greatest benefit of implementing an optimal idling policy occurs when the objective occupation rate is close to 50% in highly congested situations.     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.