分布仿真实验管理系统的设计与实现

分布仿真实验管理是分布仿真应用研究的热点和新方向,实验管理系统支持实验的设计、运行和监控,通过对分布仿真数据、模型等资源的集中存储管理和对软硬件环境的全面控制,实现对仿真实验全方位全过程的管理。分布仿真实验管理系统的推出,实现了对仿真实验的合理规划,同时也实现了对仿真系统中程序,联邦,成员和数据的运行全过程有效管理控制。相对于仿真系统传统的手工运行方式,实验管理系统不仅规范了分布仿真实验的过程、提高了实验的自动化程度,还实现了对分布仿真实验设计与运行的全面支持,降低了分布仿真系统和技术的应用门槛,可进一步促进仿真技术的应用和仿真学科的发展。该系统已成为HLA仿真系统中不可缺少的组成部分。     

USB设备的初始化分析及其扩展方法

本主要分析了USB初始化，讨论了不同USB设备类型的扩展方法，并给出了复合设备列举的特点和采用PDIUSBD12作为控制芯片的实现方法．     

General exponential smoothing and the equivalent arma process

Although the basic principles of exponential smoothing and discounted least squares are easily understood, the full power of the technique is only rarely exploited. The reason for this failure lies in the complexity of the standard procedures. Often they require fairly complex mathematical models and use a variety of cumbersome algebraic manipulations. An alternative formulation for exponential smoothing is presented. It simplifies these procedures and allows an easier use of the full range of models. This new formulation is obtained by considering the relationship between general exponential smoothing (GES) and the well-known ARMA process of Box and Jenkins. The three commonest seasonal models have only recently been considered for GES systems. They are discussed in some detail here. The computational requirements of the GES and equivalent ARMA procedures are reviewed and some recommendations for their application are made. The initialization of GES forecasting systems and the important problem of model selection is also discussed. A brief illustrative example is given.     

面向预警卫星调度问题的改进粒子群算法

针对预警卫星调度问题的特点,提出了改进型粒子群算法。首先构建了粒子整数编码和解码机制,使粒子编码对应资源分配方案;其次,采用基于优先级的粒子群初始化机制,提高算法对可行解空间的遍历效率;其次对运算符进行重定义,解决基本粒子群算法无法处理离散变量优化问题。最后将改进的离散分群粒子群算法应用于预警任务—资源的调度问题中,实验结果表明,同其它算法相比,该算法具有较高求解性能。     

-种对Bussgang类盲均衡算法的简单初始化方法

提出了一种基于短数据重用思想对Bussgang类盲均衡进行初始化的方法，这种方法使均衡器收敛的时间大大减少。与基于高阶谱进行均衡器初始化的方法相比具有需要样本短、硬件资源少和运算复杂度低的特点，且所能达到的收敛速度甚至比使用高阶谱进行均衡器初始化的方法快。     

Z_80并行接口芯片功能编程探微

本文对Z80-CPU并行接口芯片PIO、CTC的初始化编程中遇到的难点、疑点作了较系统的分析、归纳、并提出了一些自己的看法。     

主成分分析数据处理方法探讨

本文针对传统主成分分析的不足,分析了做主成分分析前对数据进行预处理的必要性,并对数据的预处理方法进行了探究,提出了“初始化”方法实现无量纲化处理数据的目的．最后结合具体实例证实了改进方法的优越性．     

Some comments on the initialization of exponential smoothing

It is shown that the traditional choice for the initial smoothed statistics in general exponential smoothing leads to the same forecasts as the equivalent ARIMA model, provided that one uses zero starting values for the initial shocks. In addition, an initialization which uses ‘backforecasts’ as initial smoothed statistics is considered, and its relationship to unconditional least squares is explored.