用于复杂系统环境的遗传分类器系统

设计了一个针对复杂系统分析的遗传分类器系统,该系统不仅利用了遗传算法强大的搜索能力和便于从大量历史数据中发现规律的优点进行分类器获取,而且还具有如下两个主要特点;①它能够适用于复杂的、缺少完整描述且训练信号带有噪声的系统环境;②它是一个白箱系统,所获取分类器的置信度与统计结果相符,从而支持定性、定量相结合的综合推理模式。     

拟带宽 Toeplitz 系统的秩 1 修正算法

本文以带宽Ｔｏｅｐｌｉｔｚ系统的快速并行解法为基础,通过建立秩１的修正格式讨论了拟带宽Ｔｏｅｐｌｉｔｚ系统的一种快速并行算法,其串行运算量为９ｎｈ＋Ｏ（ｈ）．设ｐ为处理机台数,当ｐ≤ｎ时,并行运算量为　;当ｐ＝３ｎ时,运算量为２ｈ￣２ｌｏｇ２ｎ＋Ｏ（１）,其中ｈ表示拟带宽宽度。     

熵、信息、控制与系统的组织化程度

本文首先通过分析与推演导出了描述熵、信息与系统的组织化程度之间联系的几个基本关系式;然后据之分析指出,人类的意识行为,是高度有序的主体向有序度较低的对象系统输入信息、减少其熵、提高有序度从而使其得到同化或改造的过程;最后,利用上述结论剖析了系统工程方法论的合理性与有效性。     

公度时滞系统的有理镇定补偿器

讨论了具有公度时滞系统的有理函数镇定补偿器的条件和算法.在不含时滞的名义系统为最小相位的条件下,可设计一簇常数补偿器镇定具有公度的无限时滞系统;在不含时滞的名义系统可强镇定的条件下,可设计一簇有理函数补偿器镇定具有公度的有限时滞系统.给出了相应时滞补偿器的具体算法.设计者可从这簇补偿器中选取一个最优的补偿器满足特定的设计要求.     

装备保障战备评估指标体系的建立

装备保障战备评估是一个新的概念,建立装甲装备战各评估指标体系是装备管理中的一项重要工作,通过介绍战备、评估、战备评估的概念,分析了建立装备保障战备综合指标体系的必要性,围绕装备、器材、人员、设备、维修、战备方案、战备训练、战备设施等几个方面的内容,论述了建立装备保障战备评估体系步骤和方法,实现战各工作的全程量化管理.     

用VB实现复杂控制系统原理演示的多媒体模拟

介绍了VB在多媒体编程上的优势;说明了用VB实现控制系统原理模拟的各种多媒体方法:信号流、声响效果的实现和控制;各种仪表、信号波形的动画显示;各元件及系统数学模型的建立;控制原理的实现过程等,并给出了实现的一般框图.     

Ballistic response of armour plates using Generative Adversarial Networks

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V₅₀ ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V₅₀ BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.     

基于DTRC的形式自动证明平台及其应用

动态项重写计算(DTRC)是项重写系统(TRS)的元计算模型,具有层次化结构和动态重写等特征,可应用于归纳定理的形式自动证明以及项重写系统弱终止性的形式自动证明等方面.文中介绍了一个基于DTRC的形式自动证明平台及其在TRS弱终止性自动证明上的应用.     

基于MAS的武器装备供应管理支持系统设计

对武器装备供应管理系统结构和功能进行了分析,给出系统的一般构成模式和运行要求,阐明了其所需的决策支持;以系统一般模式为背景,探讨了利用多Agent系统理论构建装备供应管理支持系统的思路,提出支持系统的组成框架,并说明了其主要特点;最后,对支持系统的信息监控过程做了具体研究,给出了相应程序。     

Optimal inventory control policy for periodic‐review inventory systems with inventory‐level‐dependent demand

We consider a setting in which inventory plays both promotional and service roles; that is, higher inventories not only improve service levels but also stimulate demand by serving as a promotional tool (e.g., as the result of advertising effect by the enhanced product visibility). Specifically, we study the periodic‐review inventory systems in which the demand in each period is uncertain but increases with the inventory level. We investigate the multiperiod model with normal and expediting orders in each period, that is, any shortage will be met through emergency replenishment. Such a model takes the lost sales model as a special case. For the cases without and with fixed order costs, the optimal inventory replenishment policy is shown to be of the base‐stock type and of the (s,S) type, respectively. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012