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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 相似文献
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为满足火力作战多样化的任务需求,结合对火力作战行动有效作用空间的具体解析,对其目标决策进行了深入的分析,在把握时代特征的同时,较为系统地总结出火力作战目标决策的新要求、新方法和新流程,为信息化条件下组织筹划火力作战提供建设性理论参考。 相似文献
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《防务技术》2022,18(9):1697-1714
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. 相似文献
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富国和强军是我国现代化建设的战略任务,是发展中国特色社会主义、实现中华民族伟大复兴的重要基石。从我党富国强军之路的探索及其引发的思考,分析了党对富国与强军、经济建设与国防建设这一重大战略思想的艰苦探索历程和理论创新轨迹,从一个侧面体现了我党对大党大国执政规律的清醒认识。 相似文献