基于广义预测一碳饱充过程的控制研究

糖厂澄清工段一碳饱充过程是一个具有强非线性、多输入等特点的复杂系统,存在很多不确定因素,所以一直以来都没有很好地解决澄清过程的稳定控制问题.针对上述问题,采用数据的建模方法,对糖厂澄清工段一碳饱充进行BP神经网络建模,然后把模型作为广义预测控制的预测模型,将广义预测控制算法对糖厂澄清工段一碳饱充进行优化控制.在Matlab上进行仿真,结果表明,BP神经网络的广义预测控制算法具有鲁棒性强、控制精度高等优点,并使糖厂澄清工段一碳饱充系统输出很快稳定到设定值,实现了优化控制,减少能耗.     

炼焦生产过程综合生产指标的改进神经网络预测方法

针对炼焦生产过程综合生产指标 (焦炭质量、产量和焦炉能耗)检测的严重滞后问题,提出一种改进BP神经网络预测方法.首先基于相关过程参数的主元分析和灰色关联分析,确定出预测模型的输入输出变量;然后采用基于改进差分进化算法的BP神经网络建立预测模型,并与基本BP神经网络预测模型进行比较;最后,对改进BP神经网络预测模型进行了验证.实验结果表明,改进BP神经网络预测模型具有较快的收敛速度和较高的预测精度,模型的预测效果可以满足生产工艺要求.     

基于ELM的硫熏强度软测量模型

硫熏强度是亚法糖厂澄清工段的关键工艺参数之一;硫熏强度过低会影响澄清效果,过高会造成成品糖二氧化硫残留过高;目前由于缺乏合适的硫熏强度在线测量装置,该参数主要采用人工取样和离线化验的检测方式,化验滞后时间较长,难以根据该指标及时指导实际生产的问题;为此文章采用极限学习机(ELLM)方法建立了硫熏强度软测量模型,并与基于支持向量机(SVM)、径向基函数(RBF)神经网络和反向传播(BP)神经网络的硫熏强度软测量模型进行对比分析;结果显示,基于ELM的硫熏强度软测量模型具有训练收敛速度快、模型精度高和泛化性能好等优点,可以满足实际糖厂澄清工段的要求.     

基于改进WNN的城市轨道交通客流量预测

针对城市轨道交通短时客流量预测问题,提出了一种基于自适应[t]分布变异的蝙蝠算法（ATM-BA）优化的小波神经网络（WNN）预测模型（ATM-BA-WNN）。在基本蝙蝠算法（BA）中引入带有线性递减控制因子的自适应[t]分布变异,使其具有变异机制,能够跳出早熟收敛。并将ATM-BA与WNN两者相互耦合,利用ATM-BA优化WNN的参数配置,进而提高WNN的预测精度。运用ATM-BA-WNN模型对郑州地铁1号线短时客流量进行预测,并与传统的WNN预测模型、BA优化的WNN（BA-WNN）预测模型以及支持向量机（SVM）预测模型进行比较。仿真结果表明,相较于其他3种模型,所建预测模型预测精度最高,拟合能力更强,误差最小,从而证明了该模型在短时客流量预测领域的可行性及优越性。     

基于BP神经网络的线切割工艺指标预测

文章研究电火花线切割工艺参数的优化预测问题.由于工艺参数与工艺指标之间的关系有很强的非线性.而人工神经网络相比传统方法能更好地摸拟切割加工参数与工艺指标之间的非线性关系.为此文章将目前应用十分广泛的BP神经网络应用于对工艺参数的预测.建立了一个基于BP网络的工艺参数预测模型,测试结果表明利用人工神经网络来预测工艺指标是一种有效的方法.     

糖厂澄清工段生产指标建模及操作参数优化

针对亚法糖厂澄清工段清汁色值和清汁残硫量难以在线测量的问题,提出了一种基于人工蜂群优化的在线极限学习机软测量方法;先用核主元分析法确定影响清汁质量的关键参数,建立基于在线极限学习机的软测量模型;同时利用人工蜂群算法对在线极限学习机的隐层参数进行寻优,优化所建模型;最后,使用带约束的粒子群对软测量模型进行优化求解,得到典型工况下的最优操作设定值,为后续工况操作提供参考依据;仿真结果表明,基于人工蜂群优化的在线极限学习机模型能够准确地预测清汁色值和残硫量,同时基于此模型优化的操作参数设定值能够达到期望的指标.     

基于改进GRU神经网络的综合能源管控系统优化研究

针对目前综合能源管控系统能耗预测的精度需求,提出一种基于改进GRU神经网络的预测优化方案。首先,考虑到GRU神经网络预测模型中超参数选取的速率直接影响着预测模型的精确度,提出采用鲸鱼优化算法对超参数进行寻优;然后将WOA算法寻优得到的超参数对GRU神经网络进行设置,再利用超参数优化后的GRU神经网络对综合能源负荷进行预测;最后将本算法和传统GRU预测模型及BP神经网络预测模型通过评价指标MAE、MPAE、RMSE进行对比。结果表明,本优化方案平均绝对误差百分比为1.79%,而传统GRU预测模型和BP预测模型的平均绝对误差百分比为3.06%、4.45%。由此得出,采用鲸鱼优化算法对GRU神经网络的改进,使得GRU预测模型更加精准和稳定。     

基于KF与ESN的糖厂澄清工段在线预测

糖厂澄清工段是甘蔗制糖的重要工艺环节之一,是一个复杂的物理、化学过程,具有非线性、大时滞、时变等特点.而且不同的榨季、甘蔗的品种、新技术的应用等情况,都可能导致过去良好的控制模型往往不能及时适应新情况的发生.基于大量离线、在线数据,结合回声状态网络(ESN)和Kalman滤波(KF)的特点,设计了应用于糖厂澄清工段的在线自适应预测方法.该方法将Kalman滤波应用于ESN的高维状态空间中,可以直接对网络的输出权值进行更新.将仿真结果与基于EKF的RBF网络相比较,说明了基于KF与ESN的糖厂澄清工段在线预测模型的优越性.     

基于经验模态分解的小波神经网络预测模型

针对小波神经网络(WNN)在非平稳、非线性时间序列预测上无法实现自适应多分辨率分析,且其预测精度有待提高的问题,提出基于经验模态分解的小波神经网络预测模型。首先,对非线性、非平稳时间序列进行经验模态分解(EMD),以降低时间序列的非平稳性;然后对EMD分析得到的固有模态分量(IMF)和余项分别构建WNN模型;最后,汇总预测结果,得到预测值。通过数据验证,新模型的预测精度高于BP神经网络和WNN。     

基于相空间重构的GQPSO-WNN短时交通流预测

交通流预测有助于减少交通拥堵和交通事故发生。在定量分析交通流变化过程的混沌特性以及可预测性基础上,提出一种基于相空间的GQPSO-WNN的混合预测模型。引入遗传算法,使用混合优化后的量子粒子群算法初始化小波神经网络的各项参数,克服网络因初始值设置不当造成无法收敛或陷入多个局部极小值的问题。由于神经网络输入的随机性,采用重新构建交通流时间序列的相空间技术,用重构后的数据作为输入样本。实验结果表明,与WNN、PSO-WNN预测模型相比,该模型可以更加准确预测交通流,算法收敛性也有明显提高。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.