A neural network model for fault detection in conjunction with a programmable logic controller

This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented.     

神经网络分类器的快速构造算法

使用ＢＰ算法训练多层网络的速度很慢而且事先难于确定隐节点和隐层的适当数目。本文提出一个有效的算法，先构造决策树，然后将构造的决策树转换为神经网。文中使用一个全局准则函数控制决策树的增长，它较好地匹配了树的复杂性和训练样本量及错分率界。实验结果，本文的算法比用ＢＰ算法训练多层网络要快，而其分类精度不低于用ＢＰ算法训练的多层神经网。     

实现CDMA最佳多用户检测的神经网络方法

提出并讨论了两种实现码分多址系统中最佳多用户检测的神经网络方法。一种基于离散Ｈｏｐｆｉｅｌｄ神经网络，另一种基于采用反向传播算法的多层感知器神经网络。理论分析和计算机模拟都证实了这两种神经网络方法的可行性，优越性和实用性。前者适用“固定”用户情况；后者既可用于“固定”用户吼适用于移动用户的情况，因而在未来的ＣＤＭＡ个人通信网中有较好的应用前景。     

Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.     

基于神经PID网络的串级调节系统研究

本文提出了一种神经PID网络控制器，并利用它构成串级调节系统。在给出网络结构和计算公式的基础上，进行了计算机仿真。仿真结果表明，神经PID网络控制器构成的串级调节系统具有良好的动态和静态性能及很强的抗扰能力。     

基于TMN的接入网网管系统模型

本文介绍了 TMN 网管系统的体系结构,进而讨论了接入网网管系统的体系结构和管理接口的有关问题。     

论网络互连

网络互连是由重发器、网桥、路由器和网关的恰当使用和适当组合而实现的．这些中继设备具有不同的性能，因此，重要的是要选择最合适的互连设备．重发器互连类似的局域网段，形成单一扩展的局域网；网桥互连类似的LAN，形成单一的子网络；路由器互连子网络，形成单一网络，网关互连不同的网络，形成集成网络．     

Neural networks,rational functions,and realization theory

The problem of parametrizing single hidden layer scalar neural networks with continuous activation functions is investigated. A connection is drawn between realization theory for linear dynamical systems, rational functions, and neural networks that appears to be new. A result of this connection is a general parametrization of such neural networks in terms of strictly proper rational functions. Some existence and uniqueness results are derived. Jordan decompositions are developed, which show how the general form can be expressed in terms of a sum of canonical second order sections. The parametrization may be useful for studying learning algorithms.This work was supported by the Australian Research Council, the Australian Telecommunications and Electronics Research Board, and the Boeing Commencai Aircraft Company (thanks to John Moore).     

广播发射集散控制系统

本文介绍了广播发射台利用计算机技术、网络技术、遥控遥测技术、现代测试手段，对设备进行自动化控制与管理；实现设备技术指标自动测试，发射台无人值班、有人留守，建立机器的计算机档案制度，确保安全优质播出的方法和经验。     

基于神经网络的VQ图象编码

文章给出将神经网络与矢量量化相结合的两种编码方法，介绍了其原理和实现的算法。经计算机模拟表明，在比特率低于０．３ｂｐｐ时仍可得到高质量的编码图象。