一种用于复杂战争过程的建模方法(Ⅰ)

本文重点介绍建模理论。现代战争系统是一个复杂系统,拥有复杂系统的所有特性,即物质、能量与信息的全面开放性,作用机制非线性和不确定性。以往的战争模型着重描述的是战场硬损耗过程,而对信息和C~3I系统等软因素缺乏描述。针对现代战争的特点我们提出了一种用于现代战争的建模方法,该方法基于复杂系统理论,能够全面描述现代战争过程的各要素,从而能够分析C~3I系统等在现代战争中的作用。     

生态农业投资项目外部效益评估的专家系统研制

将生态农业“三效益”扩充为“五效益”,并根据“五效益”最佳统一的原则,用人工智能语言－Ｐｒｏｌｏｇ编制推理机,用Ｃ语言编制分析机,进而将两结合构筑专家决策系统,系统可在不同段资规模、不同技术水平及不同投资时机等条件的约束下对生态投资项目外部效益进行主估。     

全局DEA模型研究

在传统ＤＥＡ的基础上提出了全局ＤＥＡ模型,该模型不仅克服了传统ＤＥＡ的局限性,而且以整体效率最优为出发点来优化决策单元的投入和产出分量。案例分析表明此模型切实可行,拓宽了ＤＥＡ的应用。     

通信电子战信号反演处理——I：概论

通信电子战信号处理迫切需要一个具有宏观指导意义的方法论。反演方法既是地球物理学中的基本处理方法,又是人类两大基本推理方法的一个子方法。讨论了反演方法及其理论的一般定义。指出在电子学与通信及其信号处理领域,将问题统一到信号反演的框架下,不仅有利于对处理方法的理论研究,而且有利于吸收那些在非电子学领域已经建立的反演方法及其理论。给出了关于通信电子战信号反演处理的定义,并提出其概念模型和目标参数。     

联邦接口规范测试方法研究

高层体系结构（HLA：High Level Architecture)可以支持各种类型仿真之间的互操作，同时便于建模与仿真部件的可重用。开发HLA应用必须根据联邦开发与执行过程模型进行，其中联邦测试是FEDEP模型的重要组成部分，主要包括三级测试，即：一致性测试，集成测试，联邦测试。一致性测试主要包括接口规范测试，OMT测试，RTI测度等内容，本文对联邦接口规范测试的方法进行了研究，根据接口规范的测试标准，制定了测试顺序，给出了联邦接口规范的测试顺序，给出了联邦接口规范的测试顺序模型，使接口规范测试更加系统，清晰。     

基于C-R模糊模型的预测控制的动态特性分析

提出了C-R模糊模型的辩识递推算法和C-R模糊模型的非缌挂预测控制算法。文中给出了C-R模糊模型的预测控制的动态特性分析，并得到了与线性预测控制理论相对应的C-R模糊模型的非线性预测控制稳定性定理，它对分析非线性预测控制具有一定的指导意义。     

基于维纳过程的维修决策和备件库存联合优化

为了降低设备运营总成本,充分发挥视情维修的优势,开展了设备的视情维修决策与备件库存量的联合优化研究。首先,采用Wiener过程建立了退化模型,估计了设备退化参数和剩余寿命;以此为基础,为充分利用剩余寿命的信息价值,引入预约备件的概念,选用遗传算法和蒙特卡罗相结合的方法,建立了以库存参数、预防性检测阈值和预约备件剩余寿命阈值为变量,以部件平均费用率最小为目标的维修与备件的联合优化仿真模型;最后,通过实例比较分析,表明联合优化方法大大地降低了维修和备件总成本,验证了方法的可行性和有效性。     

Higher-order principal component pursuit via tensor approximation and convex optimization

Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit （PCP）. This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit （HOPCP）, since it is critical in multi-way data analysis. Unlike the convexification （nuclear norm） for matrix rank function, the tensorial nuclear norm is stil an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme： the augmented Lagrangian alternating direction method （ALADM） and its truncated higher-order singular value decomposition （ALADM-THOSVD） version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing.     

Codebook design for uplink of densely small cells

The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing in a DSC system for uplink of the DSC system. To further improve the uplink performance, the high-rank codebook is designed based on singular value decomposition （SVD） due to the unnecessary preservation of strict constant modulus in the DSC system. And according to the simulation result, the proposed codebook leads to significant sum-rate gain and appreciable block error rate （BLER） performance improvement in the DSC system.     

Chaos and Nonlinear Feedback Control of the Arch Micro-Electro-Mechanical System

This paper addresses a nonlinear feedback control problem for the chaotic arch microelectro- mechanical system with unknown parameters, immeasurable states and partial state-constraint subjected to the distributed electrostatic actuation. To reflect inherent properties and design controller, the phase diagrams, bifurcation diagram and Poincare section are presented to investigate the nonlinear dynamics. The authors employ a symmetric barrier Lyapunov function to prevent violation of constraint when the arch micro-electro-mechanical system faces some limits. An RBF neural network system integrating with an update law is adopted to estimate unknown function with arbitrarily small error. To eliminate chaotic oscillation, a neuro-adaptive backstepping control scheme fused with an extended state tracking differentiator and an observer is constructed to lower requirements on measured states and precise system model. Besides, introducing an extended state tracking differentiator avoids repeated derivative for the virtual control signal associated with conventional backstepping. Finally, simulation results are presented to illustrate feasibility of the proposed scheme.