Optimal index shooting policy for layered missile defense system

In order to cope with the increasing threat of the ballistic missile(BM)in a shorter reaction time,the shooting policy of the layered defense system needs to be optimized.The main decisionmaking problem of shooting optimization is how to choose the next BM which needs to be shot according to the previous engagements and results,thus maximizing the expected return of BMs killed or minimizing the cost of BMs penetration.Motivated by this,this study aims to determine an optimal shooting policy for a two-layer missile defense(TLMD)system.This paper considers a scenario in which the TLMD system wishes to shoot at a collection of BMs one at a time,and to maximize the return obtained from BMs killed before the system demise.To provide a policy analysis tool,this paper develops a general model for shooting decision-making,the shooting engagements can be described as a discounted reward Markov decision process.The index shooting policy is a strategy that can effectively balance the shooting returns and the risk that the defense mission fails,and the goal is to maximize the return obtained from BMs killed before the system demise.The numerical results show that the index policy is better than a range of competitors,especially the mean returns and the mean killing BM number.     

基于偏好DEA模型的应急资源优化配置

从应急系统中应急资源的投入产出的整体相对效率考虑, 提出了新的资源优化配置的非参数DEA模型,对应急系统中应急资源总体利用情况进行了评价.在此基础上,考虑到决策者的偏好信息,构造了偏好的DEA资源配置决策模型.最后,通过对一个算例的具体分析,清楚地表明,在资源总量控制的情况下,利用本文提出的非参数偏好DEA模型对应急资源进行统筹安排和合理配置,可以大大提高应急系统的整体相对效率.     

Timing Prediction Error Volatility and Dynamic Asset Allocation

We solve a portfolio selection,problem in which,return predictability,risk predictability and transaction cost are incorporated.In the problem,both expected return,prediction error volatility,and transaction cost are time-varying.Our optimal strategy suggests trading partially toward a dynamic aim portfolio,which is a weighted average of expected future tangency portfolio and is highly influenced by the common fluctuation of prediction error volatility(CPE).When CPE is high,the investor would invest less and trade less frequently to avoid risk and transaction cost.Moreover,the investor trades more closely to the aim portfolio with a more persistent CPE signal.We also conduct an empirical analysis based on the commodities futures in Chinese market.The results reveal that by timing prediction error volatility,our strategy outperforms alternative strategies.     

An impact angle constraint integral sliding mode guidance law for maneuvering targets interception

An integral sliding mode guidance law(ISMGL)combined with the advantages of the integral sliding mode control(SMC)method is designed to address maneuvering target interception problems with impact angle constraints.The relative motion equation of the missile and the target considering the impact angle constraint is established in the longitudinal plane,and an integral sliding mode surface is constructed.The proposed guidance law resolves the existence of a steady-state error problem in the traditional SMC.Such a guidance law ensures that the missile hits the target with an ideal impact angle in finite time and the missile is kept highly robust throughout the interception process.By adopting the dynamic surface control method,the ISMGL is designed considering the impact angle constraints and the autopilot dynamic characteristics.According to the Lyapunov stability theorem,all states of the closed-loop system are finally proven to be uniformly bounded.Simulation results are compared with the general sliding mode guidance law and the trajectory shaping guidance law,and the findings verify the effectiveness and superiority of the ISMGL.     

Finite-Time and Fixed-Time Synchronization of Inertial Neural Networks with Mixed Delays

This paper considers the drive-response synchronization in finite-time and fixed-time of inertial neural networks with time-varying and distributed delays(mixed delays). First, by constructing a proper variable substitution, the original inertial neural networks can be rewritten as a first-order differential system. Second, by constructing Lyapunov functions and using differential inequalities,some new and effective criteria are obtained for ensuring the finite-time synchronization. Finally, three numerical examples are also given at the end of this paper to show the effectiveness of the results.     

Optimal Grading Policies in the Online Acquisition of Used Products

Although online reverse commerce(recommerce)is convenient and efficient,it is not without caveats.It limits recommerce firms’flexibility to offer personalized prices and may cause mismatched grading between the firms and sellers of used products.This study examines a recommerce firm’s decision on grading criteria and prices.We find that the firm’s optimal policy exhibits two distinctly different patterns depending on the trade value of the product.We demonstrate that sellers’overestimate and underestimate errors have qualitatively different effects on firm profitability,and the effects crucially rely on the type of optimal policy.These findings can apprise firms on how to preset sorting criteria and prices as well as reduce grading errors.     

Evolutionary game analysis of problem processing mechanism in new collaboration

This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the"main manufacturer-supplier"mode,which has been widely applied in the collaborative development management of the complex product.This paper adopts the collaboration theory,the evolutionary game theory and numerical simulation to analyze the decision-making mechanism where one upstream supplier and one downstream manufacturer must process an unpredicted problem without any advance contract in common.Results show that both players'decision-makings are in some correlation with the initial state,income impact coefficients,and dealing cost.It is worth noting that only the initial state influences the final decision,while income impact coefficients and dealing cost just influence the decision process.This paper shows reasonable and practical suggestions for the manufacturer and supplier in a new collaboration system for the first time and is dedicated to the managerial implications on reducing risks of processing problems.     

Metropolis-Hastings自适应算法及其应用

首先阐述Metropolis-Hastings算法实现的具体步骤,然后证明由此产生的Markov链满足细致平衡条件,从而以目标分布为不变分布.接下来给出几个计算实例,以说明提议函数及其方差的选取对采样结果的影响,并由此推出一种改进的自适应算法用以寻找合适的提议函数及其方差.最后,通过贝叶斯Logistic模型的例子说明M-H方法在贝叶斯分析中的应用,同时也检验M-H自适应算法的效果.     

地面站系统服务能力评估指标及其计算方法

针对地面站系统服务能力评估问题,研究了其评估指标及其计算方法.首先从卫星、地面站两个方面建立了地面站系统服务能力评估指标,并就各个指标以及指标的应用进行了分析.然后给出了两种指标计算方法:FCFS(First Come First Service)方法和启发式方法.最后通过实例仿真,表明该指标及方法能够较好的解决地面站系统服务能力评估问题中的是否增建新地面站、是否在某地面站中增加天线等重大决策问题.     

飞机朝后座椅的保护作用模型研究

为了进一步提高民航旅客运输的安全性和减少飞机事故的伤亡率,对飞机座椅朝后这一新概念,从安全防护的改善方面进行了系统分析和定量的描述,建立了冲撞缓冲效果的估算模型,进行了相关的简单试验,并分析了空难幸存案例,从中得出结论,如果把飞机座椅朝后安装,可以主动地、大幅度地提高事故中对人体的防护能力,减少伤害率.另外,通过比较发现,由此带来的改善,并不减少飞机原有的性能指标,也不增加成本和维护的复杂性.最后提出了分级布置飞机客舱座椅的建议,以便保留部分朝前的座椅供一些有偏好的旅客进行选择,使所有的座椅安全性都有所提高,起到了系统优化的作用.此模型为新一代面向用户的飞机设计,提供了一个系统安全分析的基础.     

Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

1. FUZZINESS Ic FAUlt DIAGNOSIS OF MISSILE WEAPON SYSTEMAs an illtegrated system based on high teclmology, a large-scale missile weapon system relates to mechanics,optics, electricity and many other fields. Naturallyl there exists fuzziness in the fault diagnosis for missileweapon system. For instance, in the hydraulic system of launching/guiding vehicle, fuzzy fault can be foundeverywhere and is difficult to judge. Possible symptoms include: the pressure fluctuates too much; …     

基于遗传模糊树的海空对抗无人机智能决策模型

随着人工智能技术的应用领域不断扩大,无人机(unmanned aerial vehicle, UAV)的自主智能化发展成为目前军事领域的研究热点。其中,UAV的武器智能决策能力是其面临舰艇编队剧烈火力打击时能否顺利完成对海侦察任务的关键条件。针对这一军事应用难点,利用改进的遗传模糊树构建UAV武器智能决策模型,以此实现UAV的智能武器决策：通过设计三模糊子集参数编码法改进参数编码方式,克服了无效编码导致的系统紊乱;采用单、组合场景结合的训练方法改进种群训练方式,提高了决策模型在多样化实战中的适应性。仿真结果表明,所提武器智能决策模型具有较好的有效性和灵活性。     

基于神经网络的导弹故障智能诊断专家系统

首先简要介绍了BP神经网络学习算法,然后在搜集专家经验和专业知识的基础上,提出了导弹故障智能诊断方法,分析了诊断参数的选取方法、知识库的建立过程及神经网络模块结构图.最后研究了基于神经网络的导弹故障智能诊断专家系统的结构及功能实现,并对某导弹发射控制系统进行了故障分析,给出了具体的诊断实例.将此方法应用于导弹武器系统故障诊断中,可提高诊断的智能性.     

复杂网络与社会网络研究范式的比较

从研究范式方面对复杂网络与社会网络研究进行比较.认为发生学视角的时间维度的分析与知识论域视觉的空间维度的分析相结合,可以提供历史与全面地认识事物的可能.首先分别从发生学及知识论域体系的视角,在比较中发现了复杂网络与社会网络研究范式的特点,然后以广州软件产业CEO社会网络与竞争关系复杂网络的研究为例,对这些特点做了具体的说明,最后进一步通过该产业的企业衍生社会网络与竞争关系复杂网络的联系,得出社会网络是产业竞争关系复杂网络的基础的结论.     

目标规划模型的知识表示与XPGP搜索算法研究

针对目标规划问题的建模与求解的问题，通过剖析目标规划数学模型的结构特征及其模型求解过程的状态转移规律，以及目标规划的求解原理与人工智能的状态空间理论分析对比，扩展人工智能状态空间三要素表示体系，提出了一种基于状态空间理论的目标规划模型的知识表示方法——Mpsp法；根据线性目标规划模型的结构和求解特点，提出了线性目标规划知识化模型改进的宽度优先搜索算法——XPGP搜索算法。本项研究使目标规划的应用研究朝着知识化、智能化方向迈进一步，充实了智能运筹学这一新学科的研究基础。     

“智能生产与服务网络”的安全界壳体系设计

"中国制造2025"战略的推进需要借助于新型产业互联网体系——"智能生产与服务网络".如何确保其稳定、有序运行?是本研究拟解决的核心问题,因此,本文首先基于"虫口模型"讨论"智能生产与服务网络"网络界壳最大卫护能力,并以此结合"界壳理论"提出构建其网络界壳体系的方案;其次讨论了其界壳开放度问题,而后引入"泛系理论"研究其界壳开放度观控问题;体系网络界壳开放度具有上限,根据实际情况可以增大或减小,必要时可降为0,以保护系统安全.最后,讨论了在一定开放度下,该体系的网络效应原理及其经济学、管理学意义.本研究从理论上拓展了"界壳理论"、"泛系理论"的应用领域,从实践上完善"智能生产与服务网络"运行机制,卫护功能,对推进我国供给侧结构性改革具有重要意义.     

可持续运营管理研究趋势和展望

可持续运营管理正成为运营管理新的重要研究领域.根据可持续运营已有文献和企业实践,本文提出可持续运营管理研究的分析框架并对所选文献样本进行关键词和网络分析、聚类,总结出可持续运营管理研究领域中的子领域.在此基础上从研究边界,研究视角和研究主题三个角度,总结和梳理出可持续运营管理的研究发展历程,即:"从内部运营到外部运营","从正向供应链到逆向供应链","可持续运营研究主题的演变",并进一步探讨已有研究中仍未解决的问题.最后针对已有研究的发展提出三个未来的研究趋势,即:"从供应链到价值链","正-逆向供应链集成"和"经济、环境和社会协同发展".以期能为可持续运营管理研究提供启示.     

双向循环进化的实体链接及知识推理框架

为体系化、智能化提升自然语言处理中实体链接任务的准确率, 同时解决图谱噪声混杂、关系稀疏等问题, 基于第三代人工智能知识&数据双轮驱动思想提出一种双向循环进化的实体链接与知识推理框架。基于知识图谱下的实体链接技术设计正向进化模块, 基于知识推理与元学习等技术设计反向进化模块, 多次循环双向控制过程实现两任务在弱监督下自我迭代和智能升级。实验表明, 在模块化设计加持下, 该框架可从特定领域文本习得特定领域知识, 并实现快速增量更新, 有效提升实体链接、知识推理效率; 同时兼具开放性, 为各业务领域小样本下文本分析能力的迭代升级提供新方法。     

知识库存对SNS企业知识社区中E-Learning知识获取的影响

SNS (social network service, SNS)企业知识社区的认知、应用以及研究都处于起步阶段. 本文研究目的是通过构建E-Learning知识获取过程计算模型, 结合实验仿真, 研究知识库存对SNS企业知识社区中E-Learning知识获取关键因素的影响. 首先, 识别SNS企业知识社区中E-Learning知识获取的关键因素,包括知识获取目标、知识获取基础和知识获取绩效; 接着, 基于知识获取关键因素, 应用多主体建模思想, 建立E-Learning知识获取的计算模型; 第三, 进行实验仿真, 以知识需求为驱动, 研究微观层面上的个体E-Learning知识获取行为经过知识交互后涌现出来的宏观特点. 研究结果有助于企业关注知识库存对SNS企业知识社区中E-Learning知识获取的影响, 采取适当策略实现企业E-Learning目标.     

决策结构设计中的通信网络问题研究

为了适应从“平台中心战”到“网络中心战”的转变,研究了决策网结构设计中的网络容量分配和网络代价问题,全面提升了决策过程和决策质量,在自适应决策结构的构造过程中,根据决策员实时通信结构,建立节点间的信息传输时间模型。在满足平均网络延迟时间最小的基础上,通过计算可获得优化的通信网络容量和网络代价。该研究结果为设计优化的决策网络结构提供了保障,对于提高网络利用率、减少网络构造成本等具有较大的应用价值。