基于信任域策略优化的末制导控制量学习算法CSCD

近年来,深度强化学习在解决序列决策问题上取得了很大进展,无模型强化学习算法在与环境不断交互的过程中学习策略,不需要提前对环境建模,使其适用于许多问题。针对以往使用强化学习进行末制导策略学习的训练不稳定问题,使用信任域策略优化算法直接学习末制导控制量,同时设计了一种新颖的奖励函数,可以提高训练稳定性和算法性能。在二维环境下进行了实验,结果表明,该算法具有良好的训练稳定性,并可以达到很好的命中效果。     

An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach

An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail.     

The decision to develop the Space Shuttle

Many of the problems that the Space Shuttle programme has had in meeting its goals of routine and cost-effective access to space can be traced to various characteristics of the decision to develop the Space Shuttle. That decision was made through a process of bureaucratic politics, with little attention given to future users of the Shuttle. The design chosen for development was a poor compromise between demanding Pentagon and NASA requirements and a limited budget.     

气象卫星的现状和未来

概述了气象卫星现况及未来发展,指出极轨气象卫星和静止气象卫星的发展方向,并对我国气象卫星系统的发展提出了建议。     

超视距空战仿真中的策略识别

针对超视距空战仿真中敌机策略的识别问题,研究了一种基于案例的策略识别方法。该方法通过构建包含假定的对手任务目标、观测数据、策略等内容的案例库,采用相似度计算选择与新的观测相似的案例库子集,并计算策略概率分布来识别对手策略。实验证明,相比与常规的基于案例推理方法,在案例中增加敌机任务目标提高了策略识别准确率,并且在假定敌机目标不正确时,能修复错误假定并进行策略识别,改善了基于案例策略识别方法的性能。     

国内民航运输市场研究成果述评

针对近年来有关中国民航运输业的相关理论研究成果,系统分析了其产业属性与市场结构、民航运输业的产业政策选择、民航运输业市场价格等热点研究领域的研究成果.基于对既有研究成果的综述,就今后研究的进一步发展方向从理论、方法的角度进行了若干展望.     

武器装备行业供应链库存优化控制研究

本研究通过分析武器装备行业供应链的特点,应用中心化的多级库存优化与控制策略,通过库存优化控制数学模型的建立、对库存点控制参数的求解以及实例分析,证明该优化方法能使供应链库存成本达到最小,缓解了需求信息放大效应带来的成本增加问题,达到了供应链的整体库存优化协调的目的.     

一种基于标签的数据安全传输策略

设计了一种基于标签的数据安全传输策略,在应用层加密数据.web服务器扩展模块和浏览器扩展模块通过标签〈secret〉和〈/secret〉确定HTTP报文中需加密或解密的数据的起始位置,加密和解密数据,保证在网络上传输的是密文,web服务器和浏览器收到的是明文.在Windows2000 ⅡS5.0 IE5.0下实现并测试通过.     

《反垄断法》框架下的民航竞争政策要点分析

民航业作为我国市场经济体制改革的示范性行业,随着历次改革持续放松管制,行业发展迅速,市场呈现繁荣景象.随着去年国家发改委启动对管制性行业的反垄断调查, 《反垄断法》适用于民航业只是一个时间问题.民航局作为行业主管部门,应保持与《反垄断法》协调一致,继续以破解行政垄断难题,放松和改进市场管制,提供技术性服务等为要点,根据行业实际情况和自身职责制定和执行民航竞争政策.     

分布式虚拟靶试系统中负载平衡机制设计与实现

针对大规模分布式交互仿真系统的开发需求,基于MAK／RTI深入研究了分布式仿真系统的负载平衡技术,并在分布式虚拟靶试系统中得到应用与验证。