Integral approximation—an approach to reduced models for particulate processes

In this contribution, a model reduction technique for population balance systems describing particulate processes is presented. This technique is based on integral approximation and allows the derivation of highly accurate moment models. In contrast to other model reduction methods which can be found in literature, this integral approximation technique can be applied for arbitrarily complex phenomena specifications. The applicability of the presented method will be demonstrated for different example processes by comparing the dynamic behavior of the original population balance models with those of the derived reduced models of moments.     

Polynomial time approximation algorithms for proportionate open‐shop scheduling

This paper deals with the presentation of polynomial time (approximation) algorithms for a variant of open‐shop scheduling, where the processing times are only machine‐dependent. This variant of scheduling is called proportionate scheduling and its applications are used in many real‐world environments. This paper develops three polynomial time algorithms for the problem. First, we present a polynomial time algorithm that solves the problem optimally if , where n and m denote the numbers of jobs and machines, respectively. If, on the other hand, , we develop a polynomial time approximation algorithm with a worst‐case performance ratio of that improves the bound existing for general open‐shops. Next, in the case of , we take into account the problem under consideration as a master problem and convert it into a simpler secondary approximation problem. Furthermore, we formulate both the master and secondary problems, and compare their complexity sizes. We finally present another polynomial time algorithm that provides optimal solution for a special case of the problem where .     

正态分布属性数据集上的Skyline查询

Skyline查询适用于多目标决策、数据挖掘等领域。以往的研究工作主要关注确定型数据集,少数针对不确定型数据集的研究处理的也多是离散型不确定数据。为此,定义属性呈连续型分布的不确定数据集,并给出一种针对该种数据集的Skyline查询算法,利用索引和分治思想解决其Skyline查询问题。实验结果表明,该算法可以在牺牲少量精度的情况下提高查询效率。     

受限波兹曼机联合稀疏近似的脑功能检测模型

人脑功能连通性检测是神经科学研究的重要技术．使用受限制波兹曼机（RestrictedBoltzmannMachine,RBM）对大量多被试功能磁共振（functionalMagneticResonanceImaging,fMRI）数据进行建模可以检测人脑功能连接,但是不能有效检测单被试数据的功能连接．本文研究一种新颖的融合了稀疏近似与RBM技术的脑功能连通性检测模型,该模型充分利用fMRI数据的稀疏性,采用稀疏近似理论对fMRI数据进行空间域稀疏近似压缩,然后使用RBM建立模型,以检测脑功能连通性．实验结果表明,该融合模型可以有效地提取单被试数据的脑功能时间域混合模型及其相应的脑功能图谱,解决了RBM在单被试数据分析上的瓶颈．     

基于多代表点学习的RSKNN分类算法

RSKNN 算法是一种基于变精度粗糙集理论的 k-近邻改进算法,该算法能够保证在一定分类精度的前提下,有效地降低分类的计算量,提高分类效率。但由于 RSKNN 算法只是简单地将每个类中的样本划分成一个核心和边界区域,并没有根据数据集本身的特点进行划分,因而存在极大的局限性。针对存在的问题,提出一种多代表点学习算法,运用结构风险最小化理论对影响分类模型期望风险的因素进行分析,并使用无监督的局部聚类算法学习优化代表点集合。在UCI公共数据集上的实验表明,该算法比RSKNN算法具有更高的分类精度。     

贝叶斯网络分类器近似学习算法

贝叶斯网络在很多领域应用广泛,作为分类器更是一种有效的常用分类方法,它有着很高复杂度,这使得贝叶斯网络分类器在应用中受到诸多限制。通过对贝叶斯网络分类器算法的近似处理,可以有效减少计算量,并且得到令人满意的分类准确率。通过分析一种将判别式算法变为产生式算法的近似方法,介绍了这种算法的近似过程,并将其应用在了贝叶斯网分类算法中。接着对该算法进行分析,利用该算法的稳定性特点,提出Bagging-aCLL 集成分类算法,它进一步提高了该近似算法的分类精度。最后通过实验确定了该算法在分类准确率上确有不错的表现。     

蚁群优化算法在求解随机组合优化问题中的应用综述*

不确定条件下的优化问题更贴近真实世界环境,因而日益受到广泛关注。综述了蚁群优化在求解一组不确定条件下的组合优化问题,即随机组合优化问题方面的应用。首先介绍了不确定条件下组合优化问题的概念分类模型,给出了随机组合优化问题的一般定义;然后指出了其与求解传统确定性组合优化问题的不同之处,即目标函数的计算存在不确定性,并详细论述了目前解决方法的进展;最后分析了该领域值得重点关注的几个研究方向,并对其未来发展进行了展望。     

基于四次矩阵样条函数的二阶矩阵微分方程近似解

使用四次矩阵样条函数方法来解二阶矩阵微分方程．首先对矩阵微分方程的初始问题进行介绍;然后构造四次矩阵样条函数;并对矩阵样条函数方法进行算法描述;最后通过实例和误差分析来说明四次矩阵样条函数方法的有效性．     

Improved response surface method based on sample point selection strategies

In the reliability analysis of complex structures,response surface method(RSM)has been suggested as an efficient technique to estimate the actual but implicit limit state function.A set of sample points are needed to fit to the implicit function.It has been noted that the accuracy of RSM depends highly on the so-called sample points.However,the technique for point selection has had little attention.In the present study,an improved response surface method(IRSM)based on two sample point selection techniques,named the direction cosines projected strategy(DCS)and the limit step length iteration strategy(LSS),is investigated.Since it uses the sampling points selected to be located in the region close to the original failure surface,and since it needs only one response surface,the IRSM should be accurate and simple in practical structural problems.Applications to several typical examples have helped to elucidate the successful working of the IRSM.     

粗支持向量机分类建模方法

为了克服样本模式的复杂性、噪声的影响以及信息的不完整性问题,利用粗糙集和支持向量机(SVM)的优点,把粗糙集理论用于二分类球形SVM,提出一种称为粗支持向量机分类建模方法.粗糙集具有刻画不确定、不完整数据和复杂模式的能力,分类结果能够体现出数据的不确定性,但是它不仅不具备良好的学习能力,而且也不能保证分类模型具有良好的推广能力;SVM具有良好的推广性能,但是对不确定数据的建模能力较差.本文把分类结果分为正域、边界域和负域,由此来判断不确定数据样本的分类结果的不确定性程度.通过调整参数来调节边界的宽度和允许建模的在野点样本的比例,提高分类模型的灵活性.仿真结果说明了算法的有效性.