Ada网络文件传输系统的实现

本文介绍了一个在NETBIOS支持下用Ada的任务机制实现的网络上任意两节点间的文件全双工传输系统。传输时,文件以数据报的分组形式发送,由Ada 的任务特性来管理收、发进程,实现后台全双工操作。本文还简要地介绍了实现中的一些技巧。     

神经计算机容错能力分析

容错能力是计算机(传统计算机或神经计算机)能力的一个极重要的方面。本文从研究传统计算机容错技术和人脑容错特性出发,着重研究了神经计算机的容错能力,并得出了对神经计算机设计极有价值的结论。     

神经网络在声纳目标识别中的研究进展

综述了神经网络在声纳信号预处理、目标特征提取及其自动分类方面的研究进展,简要分析了神经网络模型的特点和在应用中的局限性。并对该领域的发展趋势提出了自己的看法。     

基于BP 网络与D-S理论相结合的点目标状态下卫星及其伴飞诱饵的识别方法

本文提出的点目标状态下卫星及其伴飞锈饵的识别方法是基于ＢＰ网络与Ｄ－Ｓ理论相结合的信息融合方法。该方法采用目标的红外辐射特征,先用ＢＰ网络对目标进行粗分类,然后用Ｄ－Ｓ理论对ＢＰ网络的多次识别结果进行融合。仿真实验结果表明,Ｄ－Ｓ理论的最后输出比ＢＰ网络的输出识别率得到很大的改善,抗噪能力得到很大的提高     

数字调制信号的神经网络识别方法

提出数字调制信号的人工神经网络识别方法，从信号幅度、相位、频率及功率谱等特性中提取五种特征参数，用于训练神经网络对数字调制信号的识别。采用神经网络，不仅可提高识别的智能化，而且能提高正确识别率。     

人工神经网络方法实现自动判别失磁类型及失磁深度

利用失磁后 Ｅｔ．ｑ、 Ｅｆ ｑ．ｑ 衰减时间常数 Ｔｑ 仅取决于发电机参数及失磁类型的结论，根据失磁故障的分类，采用一个三层前向神经网络得出 Ｔｑ ，使微机失磁保护能够在失磁发生的瞬间自动判别出失磁类型并预测失磁深度。数字仿真、动模试验以及实际运行证明了本文所提出的方法。     

基于FNN和RS理论的综合评估及应用实例

为较好解决武器系统效能综合评价与分析的问题,提出一种新的综合评价与 分析方法。以某一类型的武器装备的效能评估为例,利用模糊神经网络(FNN)的方法和粗 糙集(RS)理论方法对武器装备效能的优劣进行评估。首先,用FNN方法提取用于效能评 估的模糊规则,然后用一种新的处理不确定知识的数学工具,粗糙集理论方法对装备属性进 行约简,删除其中不相关或不重要的知识,选出最重要的且尽可能少的评价指标获得系统效 能评价的最小决策算法,进而分析得到系统效能的关键因素。     

无线自组织网络跨层攻击检测博弈模型

无线自组织网络中的跨层攻击具有比单层攻击更强的隐蔽性、更好的攻击效果或更低的攻击成本.为了检测无线自组织网络中的跨层攻击,提出了一种基于博弈论的攻击检测模型.由于攻击不可避免地会对各协议层的参数造成影响,因此模型从协议层攻防博弈的角度,建立起相应的策略矩阵和支付矩阵,并通过均衡分析得到该模型的混合策略纳什均衡解.仿真结...     

Disruption management in production planning

We study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

An approximate planning model for distributed computing networks

We develop an approximate planning model for a distributed computing network in which a control system oversees the assignment of information flows and tasks to a pool of shared computers, and describe several optimization applications using the model. We assume that the computers are multithreaded, and have differing architectures leading to varying and inconsistent processing rates. The model is based on a discrete‐time, continuous flow model developed by Graves [Oper Res 34 (1986), 522–533] which provides the steady‐state moments of production and work‐in‐queue quantities. We make several extensions to Graves' model to represent distributed computing networks. First, we approximately model control rules that are nonlinear functions of the work‐in‐queue at multiple stations through a linearization approach. Second, we introduce an additional noise term on production and show its use in modeling the discretization of jobs. Third, we model groups of heterogeneous computers as aggregate, “virtual computing cells” that process multiple tasks simultaneously, using a judiciously selected control rule. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.