基于HPSO的钢坯加热过程炉温优化设定

针对一种蓄热推钢式加热炉三个加热区的炉温稳态优化问题,本文提出了一种混合粒子群优化(HPSO)方法.首先,基于钢坯导热偏微分方程和边界条件,建立钢坯温度预报模型.然后,采用HPSO算法确定最佳稳态炉温,即炉温控制的参考输入.该方法利用混沌机制产生初始种群,通过免疫和克隆来提高粒子群优化(PSO)算法的全局搜索能力和搜索精度.     

基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

热处理炉钢板温度的自适应混沌粒子群算法–最小二乘支持向量机优化预报算法

针对传统传热模型参数调整较复杂和模型精度较低的问题,构建了一种基于改进粒子群算法优化最小二乘支持向量机（least squares SVM,LSSVM）的钢板温度预报模型.首先,对基本粒子群算法进行分析,提出自适应混沌粒子群算法（adaptive chaos PSO,ACPSO）,并通过性能指标定量评价验证算法的有效性、鲁棒性和寻优效率.其次,采用LSSVM建立钢板温度预报模型,并选用径向基函数作为核函数,用ACPSO算法优化该模型参数.最后,结合现场数据进行仿真研究和工程应用,结果表明基于该算法建立的钢板温度预报模型具有较高的预报精度,达到智能调优的目的.     

基于grey Markov--支持向量机的电弧炉终点参数预报

考虑电弧炉终点参数既受定量因素的影响,又受非定量因素的影响.将灰色马尔可夫 (grey Markov)与支持向量机 (SVM)相结合.建立了电弧炉终点参数grey Markov-SVM预报模型,其中grey Markov模型反映非定量因素对电弧炉终点参数预测值的影响,SVM模型反映电弧炉各种定量输入对终点参数预测值的影响.建立grey Markov-SVM模型的方法是:首先建立反映非定量因素的GM (1,1)模型,然后用Markov链修正其预测值:由于grey Markov模型对定量输入的影响无法准确反映,因此grey Mar-kov模型必然存在预测偏差,此预测偏差通过建立反映定量输入与终点参数预测偏差之间关系的SVM模型方法加以补偿,并采用粒子群算法 (PSO)对SVM的参数进行寻优,最终得到电弧炉终点参数的预报值,同时实现滚动预测.仿真实验表明grey Markov-SVM模型与grey-SVM模型、Markov-SVM相比较,具有很高的终点预报精度.     

基于PSO-LSSVM的城市时用水量预测

为解决传统最小二乘支持向量机(LSSVM)采用交叉验证确定参数耗时较长和粒子群(Particle Swarm Optimization,PSO)优化算法早熟收敛的问题,提出一种基于种群活性PSO算法优化LSSVM参数的方法。利用群活性加速度作为多样性测度,当群活性加速下降时,对粒子的位置和速度分别执行进化和变异操作来改进标准PSO算法,然后分析上海市时用水量序列特点及其影响因素,选取影响程度较大的主要因素,将其作为预测模型的输入变量,建立时用水量预测模型;最后采用改进的PSO算法优化LSSVM参数来预测上海市时用水量。实例分析表明,对比文中其他3种模型输入变量组合,选取的预测模型输入变量能够更有效地提高预测精度;与传统LSSVM方法相比,提出的基于改进PSO-LSSVM的时用水量预测方法计算速度更快,预测精度更高。     

基于SVM算法的文本分类技术研究

在优化分类技术的研究中,文本特征化后通常具有高维性和不平衡性的特点,导致传统的分类算法准确率不高的问题.针对文本分类器的性能容易受到核函数和参数的影响的问题,为提高文本分类器的准确性.采用支持向量机(SVM)的理论在文本分类技术同时将根据优化的粒子群算法(PSO)引入SVM分类算法中进行优化文本分类器的参数,将分类器的准确率作为PSO算法适应度函数通过粒子移动操作找出最佳参数并用SVM算法进行分类.在文本数据集上的仿真结果表明,与传统的算法相比,经PSO算法优化后的SVM文本分类器的准确性更高,PSO算法是一种有效的优化方法,能广泛应用于文本分类问题.     

神经网络在股票价格预测中的研究

研究准确优化预测股票价格问题,针对影响股票价格具有非线性、不稳定的特征,股票价格由于受到社会经济因素的影响,变化大.采用传统神经网络方法在股票价格预测中易陷入局部极小值,泛化能力受到影响.为了提高股票价格精度,提出一种基于粒子群优化算法(PSO)的RBF神经网络(RBFNN)股票价格预测模型.利用粒子群优化算法的良好的寻优能力,对RBF神经网络参数进行优化,从而加快RBF神经网络运算速度,并提高了RBF神经网络的预测精度.利用粒子群优化的RBF神经模型对上证指数(000001)股票价格进行了验证性测试和分析,实验结果表明,相对于各参比模型,经过粒子群优化的RBF神经网络模型预测方法有更好的收敛性,更强的学习能力,显著地提高了预测精度,可为预测提供依据.     

一种用于高光谱遥感影像分类的改进多类支持向量机

将支持向量机(SVM)用于高光谱遥感影像分类的研究,采用决策边界特征提取(DBFE)算法对高光谱影像进行维数约简,以径向基函数(RBF)作为SVM模型的核函数,把混沌优化搜索技术引入到PSO算法中,以基本PSO算法为主体流程,对种群中最好的粒子进行给定步数的混沌优化搜索,以改进基本PSO算法进化后期收敛速度慢、易陷入局部极小值的缺陷。利用改进的混合粒子群优化算法(PSO)来实现SVM模型参数的自动选择,继而构建了一种参数最优的粒子群优化支持向量机(PSO-SVM)多类分类模型。选用220波段的AVIRIS高光谱遥感影像进行了分类试验。结果表明,与采用基于留一法(LOO)网格搜索策略的传统SVM相比,改进后的PSO-SVM算法可以提高分类精度约8.8%。该方法对于小样本、非均衡条件下的遥感影像数据分类非常有效。     

加权因子的PSO-SVR区域空气PM2.5浓度预报方法

结合支持向量回归机(SVR)和粒子群优化算法(PSO),本文提出了一种加权因子的区域大气PM2.5浓度预测方法(W-PSO-SVR)。该方法对预测模型的输入变量进行[0,1]间的不平等加权赋值,权重值由PSO寻优求得,通过不断寻优迭代,赋予输入变量不平等的权重,从而建立预测模型。对区域大气PM2.5浓度预报结果表明,与单独的支持向量回归机模型和0或1的加权因子的支持向量回归模型相比,W-PSO-SVR预报精度提高明显,且该方法不用考虑对历史数据时滞因子的影响,较好地实现了模型输入参数的有效选择,从而可降低输入参数的维数。     

基于PSO优化SVM的转炉炼钢用氧量预测研究

用氧量是影响钢水质量的主要因素之一,为提高转炉炼钢用氧量模型的预测精度,提出基于PSO优化SVM的吹氧量建模预测方法。针对SVM结构参数依据经验选取,致使预测模型的泛化能力差,在标准PSO算法的基础上,优化SVM的惩罚系数、不敏感损失系数和高斯核宽度系数3个结构参数,并建立转炉炼钢用氧量预测模型;在此基础上利用UCI数据库中的Auto-MPG标准数据,验证了方法的有效性;最后以某钢厂100 t转炉的实际生产数据建立吹氧量预测模型,结果表明,与标准BP、RBF及SVM相比,基于PSO优化SVM的转炉炼钢吹氧量预测模型精度高、泛化能力强。     

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).