一种多输入队ATM交换结构

本文提出一种多输入排队ＡＴＭ交换结构和相应的输出冲突解决方法，它可以使交换网络避免输入队固有的排头阻塞，从而使网络在不增加运行速率的情况下，可达到输出排队的性能。文中介绍了冲突解决办法及其实现技术，说明它可方便地在高速网中实现。     

Netware的数据封锁机制与应用程序设计

文章较详细地讨论了Ｎｅｔｗａｒｅ的数据封锁机制，并介绍了Ｎｅｔｗａｒｅ应用程序的设计方法。     

解线性及二次型规划问题增广的神经网络

本文提出了一个解线性及二次型规划问题的神经网络模型，证明了该网络是全局稳定于平衡点，而平衡点就是线性及二次型规划问题的解，该网络的优点是能够实时获得问题的精确解，且可以同时获得带等式不式约束的对偶问题解，该网络易于电路实现。     

FDDI及其网络规划设计

从参考模型等方面阐述了FDDI标准，讨论了FDDI的主要组网方式，并以实例阐述了网络的规划设计。     

中国爆破安全网

中国爆破安全网是由中国工程爆破协会主办的涉爆行业信息专业网 ,开通于 2 0 0 3年 4月。该网是民爆器材生产流通主管部门、使用部门和公安机关实施联网监控和规范化管理的有力工具 ,最主要的功能是为涉爆行业管理部门和从业单位提供一个功能完善的管理和办公平台。该网的开通 ,有利于民爆器材生产、销售、购买、使用流向监控制度的贯彻实行。文章介绍了网络的组成、建设原则 ,并给出了中国爆破安全网网络拓扑图     

无约束淬火机目标温度神经网络预报

淬火、控制冷却的冷却速度和目标温度是关系到钢板性能的关键因素 ,但由于冷却过程的水量、水温、辊道速度等对冷却目标温度的影响属多变量非线性过程 ,用简单的解析模型通常精度较低 ,难以达到精确控制的要求。采用人工神经网络技术对无约束淬火机目标温度进行快速预报 ,结果表明 ,预测精度高 ,快速准确     

Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.     

Automated model generation for performance engineering of building automation networks

Large technical systems need to be designed both reliable and efficient. Specialized design tools offer therefore a simplified, abstract design and extend details autonomously in the background. Analytic and simulation based models could improve the quality by testing and dimensioning the design before implementation, but setting up the necessary models is time-consuming and expensive. Therefore many developers ask for analysis tools which are able to create their models from the available information in the design tools. This paper presents such an automated modeling approach basing on an existing design database at the example of a network analysis for building automation fieldbuses. The process of automated modeling is unfolded, and the potentials and limitations are discussed.     

Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.     

On‐Line Detection Of State‐Of‐Charge In Lead Acid Battery Using Radial Basis Function Neural Network

To realize a stable supply of electric power in an automobile, an accurate and reliable detection method of SOC (state‐of‐charge) in a lead acid battery is required. However the dynamics of the battery is very complicated. The characteristics of the battery greatly change due to its degradation. Moreover a automobile has many driving patterns, which are unknown beforehand. Thus it is not easy to detect the SOC analytically. In this paper, to overcome this problem, a new on‐line SOC detection method with a radial basis function neural network is proposed. In order to increase the detection accuracy of degraded batteries, physical values related to the degradation degree are used as input signal in the neural network. The detection accuracies for different sized batteries and various degradation states are investigated.