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.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

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.     

编辑出版自动化技术在医药专业期刊编辑部的初步应用探讨

本文首先概述了编辑出版自动化技术的概念及最新进展,然后就其在医药专业期刊编辑部的初步应用进行了探讨,论述了计算机、网络、数据库技术在稿件的选题、组稿、编辑、审校、排版、印刷、出版中的具体应用,强调了构建基于网络的编辑出版自动化系统对期刊现代化建设的重要作用。     

关于成都膨胀土地区边坡稳定性的探讨

成都市三环路建设中 ,膨胀土边坡稳定性研究是大家关心的重要课题。膨胀土边坡土体中的吸力变化直接影响到其稳定性 ,参照“人工神经网络技术”中的吸力经验公式 ,以三环路东一B标段的路堤边坡为例 ,采用“条分法”进行预测分析 ,并有针对性地进行防护处理取得了较好的效果。     

智能小区中的网络和布线设计解决方案——智能建筑学术讨论系列文章之七

当前小区信息网络建设中存在多种实施方案。笔者分析比较了常用的几种典型方案的优劣。讨论了目前建设中的一些解决方法，提出了兼顾现状，适度超前的设计实施原则。     

分散网络制造浅谈

网络社会的制造业将是一个全球化的分散型的制造体系.分散网络制造是基于互联网的制造活动,因此,它具开放的、虚拟的,以及资源和技术更合理配置的特点.其组织模式是多种多样的,按现今国防工业现状,宜采用项目领导小组统一协调和控制下的分阶段动态联盟更为合适.为此,分析了网络无序化、企业属性知识不足、利益分配不易等问题带来的困难,并提出了一些建议.     

General rate equations and their applications for cyclic reaction networks: multi-cycle systems

The network reduction technique and the Bodenstein approximation of quasi-stationary behavior of reaction intermediates were systematically applied to derive general yield ratio and rate equations for multi-cycle reaction networks in homogeneous catalysis. Dual-cycle reaction networks connected by a linear pathway, multi-cycle networks stemming from the same intermediate, and single-cycle with arbitrary number of pathways between two intermediates were considered. The general yield ratio and rate equations derived in this study are applicable for most enzymatic reactions and for homogeneous catalytic reactions. Examples of homogeneous catalysis were used to illustrate the application of the general yield ratio and rate equations for network elucidation.