基于改进灰狼算法和多核极限学习机的铁水硅含量预测建模

针对高炉铁水硅含量难以在线检测的问题, 本文提出一种基于改进灰狼算法(IGWO)优化的多核极限学习 机(MKELM)高炉铁水硅含量预测建模方法. 首先, 针对灰狼算法(GWO)寻优能力的不足, 将最优–最差正交反向学 习(OWOOBL)策略应用于灰狼算法的位置更新, 得到一种改进灰狼优化算法. 通过10种标准函数对所提算法进行 仿真测试, 结果表明此算法具有更好的寻优能力. 其次, 针对单核极限学习机(KELM)回归能力不足, 将不同种类的 核函数加权组合, 并采用改进灰狼算法对多核极限学习机中的加权系数等参数进行优化. 最后, 基于某钢厂的实测 数据对高炉铁水硅含量进行预测建模, 仿真结果表明, 本文所提方法的预测效果优于反向传播神经网络(BP–NN)、 极限学习机(ELM)、KELM和GWO–MKELM, 对高炉炼铁具有较好的指导意义.     

PCA和PSO-ELM在高炉铁水硅含量中的预测仿真

炉温的实时预测技术对高炉运转具有重要意义。在高炉炼铁过程中,通常以铁水硅含量来表征高炉热状态。针对硅含量预测效率和精度不足的问题,提出主成分分析和粒子群改进的极限学习机相结合的方法对高炉铁水硅含量进行预测。由于影响铁水硅含量的因素众多,且各因素之间相互影响,通过主成分分析对影响硅含量的输入变量进行降维处理。利用粒子群算法来优化极限学习机的权值和阈值,并以均方根误差作为适应度函数建立预测模型。将提取出的主成分作为模型输入,铁水硅含量作为模型输出。最后比较了极限学习机算法和粒子群改进的极限学习机,实验结果表明改进后的预测模型提高了硅含量预测的准确度,上述方法可为高炉的生产操作提供参考。     

高炉铁水硅含量预报的ICA-SVM建模方法

提出了一种基于改进的动态独立分量分析（independent component analysis, ICA）和支持向量机(support vector machine, SVM)的高炉铁水硅含量预报模型建模方法．采用动态ICA方法对样本数据进行特征提取,消除生产工艺参数之间的相关性．在此基础上,再使用目前计算复杂性较小的最小二乘SVM算法建立高炉铁水硅含量预报的动态递推模型,并引入了遗传算法以优化模型性能．以某钢厂高炉实际生产数据进行了应用实验,并与现有的时间序列分析、人工神经网络和基本SVM建模方法进行了对比．实验统计结果表明,本文方法显著提高了铁水硅含量的预测命中率．     

铁水硅含量的混沌粒子群支持向量机预报方法

提出一种基于混沌粒子群优化(CPSO)的支持向量回归机(SVR)参数优化算法, 并使用该算法建立高炉铁水硅含量预测模型(CPSO–SVR), 对某大型钢铁厂高炉铁水硅含量的实际采集数据进行预测, 结果表明基于混沌粒子群优化算法寻优的参数建立的铁水硅含量支持向量回归预测模型具有良好的预测效果. 与最小二乘支持向量回归机(LS–SVR)、使用粒子群优化算法训练的神经网络(PSO–NN)进行比较, CPSO–SVR模型对铁水硅含量进行预测时预测绝对误差小于0.03的样本数占总测试样本数的百分比达到90%以上, 预测效果明显优于PSO–NN, 且比LS–SVR稳定性更强, 可用于高炉铁水硅含量的实际预测, 表明混沌粒子群优化算法是选取SVR参数的有效方法.     

复杂高炉炼铁过程的数据驱动建模及预测算法

高炉炼铁过程的控制意味着控制高炉铁水温度及成份在指定的范围. 本文以高炉炉内热状态的重要指示剂---高炉铁水硅含量为研究对象, 针对机理建模难以准确预测、控制高炉铁水硅含量的发展变化, 利用数据驱动建模的思想, 建立了基于多元时间序列的高炉铁水硅含量数据驱动预测模型. 实例分析表明, 建立的数据驱动预测模型能够很好地预测高炉铁水硅含量, 连续预测167炉高炉铁水硅含量, 命中率高达83.23%, 预测均方根误差为0.07260. 这些指标均优于基于单一硅时间序列所建立的数据驱动模型, 对实际生产具有很好的指导作用.     

基于差分进化优化ELM的模拟电路故障诊断

极限学习机具有学习速度快、精度高的优点。为了进一步提高泛化能力,将差分进化算法的全局寻优和算法简单的特点引入到极限学习机的参数优化中,建立了基于差分进化算法优化极限学习机的模型,使两种算法的优点有机结合,应用于模拟电路故障诊断中。首先利用主元分析对电路采样信号进行处理,提取故障特征;其次利用差分进化算法的全局寻优能力优化极限学习机网络的权值和阈值,具有很好的泛化能力。此方法应用于电路仿真实例中,能在较短的时间内获得满意的结果。     

基于FWADE-ELM的短时交通流预测方法

受道路环境和人为因素影响,实际交通系统可视为一个复杂的非线性动力系统,交通流数据具有较强的非线性、时变性和易受随机噪声影响等特征.针对复杂环境下的短时交通流预测问题,提出一种基于烟花差分进化混合算法-极限学习机的短时交通流预测方法.采用奇异谱分析方法滤除原始交通流数据中包含的噪声成分,降噪后的交通流数据用于训练极限学习机(ELM)网络预测模型.进行相空间重构,利用C-C算法确定ELM网络的结构和关键参数.通过融合烟花算法和差分进化算法提出一种烟花差分进化混合算法,可有效提高基本算法的整体优化性能.将改进的混合优化算法用于优化ELM网络的权阈值(结构为9-11-1,维数为110),建立短时交通流预测模型.测试与应用结果表明,所构建的短时交通流预测模型具有较高的预测精度和较强的泛化能力(均方误差为7.75,平均绝对百分比误差为0.086 7),预测值与实际值的拟合程度较好.     

基于飞参数据的飞机操纵系统故障评估方法

针对某型飞机的操纵系统故障评估问题,提出了一种基于飞参数据建立的差分进化极限学习机(DE-ELM)算法。该算法融合了差分进化(DE)和极限学习机(ELM)两种算法,通过对飞参数据进行训练,构建了飞机操纵系统的黑箱模型。由于极限学习机(ELM)的输入权值以及隐含层阈值是随机产生的,所以ELM的随机性较大,稳定性不高,故利用寻优能力较强的DE对ELM输入权值和隐含层阈值进行寻优,从而实现ELM的结构优化,提升ELM的稳定性和鲁棒性。仿真结果表明,DE-ELM算法的决定系数达到了97.6%,其均方误差相比于BP神经网络降低了约79%,相比于单纯的ELM降低了64%。所以说该法可以有效提高精确度,同时具有更加良好的泛化性能。     

基于Bootstrap的高炉铁水硅含量二维预报

高炉铁水硅含量的实时准确预报对调控高炉炉温和稳定炉况具有重要作用, 但其预报结果一直存在准确度不高和缺乏可信度表征等问题, 特别是在炉况不稳、运行数据波动较大时, 预报结果的准确度和可信度急速下降, 不利于现场操作人员根据预报结果进行生产操作. 为此本文融合神经网络和Bootstrap预报区间方法, 构建高炉铁水硅含量的二维预报模型, 实现在预报硅含量值的同时给出了该预测值的可信度.应用实例表明, 本文提出的方法提高了硅含量点预测结果的准确度, 且预测区间宽度能正确地表征点预测结果的可信度, 对实际生产操作具有较好的指导意义.     

基于KPCA和ELM的新型民机舵机系统故障诊断

针对传统极限学习机模型难以学习高维数据、输入权值矩阵和隐含层偏置随机给定的问题,利用核主成分分析法来降低民机故障数据维数,通过改进的极限学习机来建立新型民机升降舵故障诊断模型.模型利用差分进化算法改进了极限学习机输入权值矩阵、隐含层偏置随机生成的缺点,应用到新型民机升降舵系统的故障诊断中.测试结果表明,KPCA-ELM...     

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。     

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.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Mr. Twinn’s bombes

This article discusses the history and design of the special versions of the bombe key-finding machines used by Britain’s Government Code & Cypher School (GC&CS) during World War II to attack the Enigma traffic of the Abwehr (the German military intelligence service). These special bombes were based on the design of their more numerous counterparts used against the traffic of the German armed services, but differed from them in important ways that highlight the adaptability of the British bombe design, and the power and flexibility of the diagonal board. Also discussed are the changes in the Abwehr indicating system that drove the development of these machines, the ingenious ways in which they were used, and some related developments involving the bombes used by the U.S. Navy’s cryptanalytic unit (OP-20-G).