灰色GM(1,1)模型在井壁破裂中的应用研究

利用灰色GM(1,1)模型 ,把井壁破裂治理中及治理后所获得的数据视为灰色量 ,并用数列方法进行数据生成、数据拟合和数据预报 ,进而指导井壁治理工作的进展和长期维护 .算例结果表明 ,该模型具有较高的拟合和预测精度 ,可以作为井壁破裂综合治理的一种实用的辅助方法     

一种基于主元递归分析法的多模糊逻辑系统的组合形式

根据多个模型相加可以提高整体预测精度和鲁棒性的思想，提出了一种具有递阶特点的模糊逻辑模型。该模型采用基于山峰函数的减法聚类算法，将样本数据集分成多组来进行局部模糊模型的建立和训练，大大提高了组合模糊逻辑模型的训练效率。各局部模糊系统的预测输出通过主元递归分析法（PCR）连接，解决了模型之间的严重相关性问题，增强了模型的预测能力，提高了模型的鲁棒性。仿真结果表明，组合多个模糊逻辑模型能够达到比局部模型更好的建模效果，并能有效的改善模型的预测能力和泛化能力。     

Direct multicriteria clustering algorithms

In a multicriteria clustering problem, optimization over more than one criterion is required. The problem can be treated in different ways: by reduction to a clustering problem with the single criterion obtained as a combination of the given criteria; by constrained clustering algorithms where a selected critetion is considered as the clustering criterion and all others determine the constraints; or by direct algorithms. In this paper two types of direct algorithms for solving multicriteria clustering problem are proposed: the modified relocation algorithm, and the modified agglomerative algorithm. Different elaborations of these two types of algorithms are discussed and compared. Finally, two applications of the proposed algorithms are presented. Elaborated version of the talks presented at the First Conference of the International Federation of Classification Societies, Aachen, 1987, at the International Conference on Social Science Methodology, Dubrovnik, 1988, and at the Second Conference of the International Federation of Classification Societies, Charlottesville, 1989. This work was supported in part by the Research Council of Slovenia.     

柴油喷雾粒子全息再现像的图像处理

本研究从分析喷雾再现粒子像的光场分布出发,探讨了灰度阈值的选取对粒径测量误差的影响.通过对粒子图像直方图的实验研究分析,提出了灰度阈值的自动选择方法和实现了多阈值自动分割,以解决不均匀背景粒子再现像的测量精度问题.通过对粒子再现像的调焦过程光场分布计算分析和试验研究,提出了一个简单可行的判焦准则,利用此准则可以加快判焦速度.     

灰色关联度分析在矿井水防治中的应用

本文应用灰色关联度分析方法,定量地评价矿井充水水源,并讨论了灰色关联度分析的基本原理,分析了子序列与参考序列的关系;并通过工程实例介绍了均值处理及关联系数的计算方法。     

Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section

This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. The higher mark the clustering result gains, the higher quality it has. By organizing two modes in different ways, we can build two clustering algorithms： SECDU（Self-Expanded Clustering Algorithm based on Density Units） and SECDUF（Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section）. SECDU enumerates all value pairs of two parameters of clustering mode to process data set repeatedly and evaluates every clustering result by evaluating mode. Then SECDU output the clustering result that has the highest evaluating mark among all the ones. By applying ＂hill-climbing algorithm＂, SECDUF improves clustering efficiency greatly. Data sets that have different distribution features can be well adapted to both algorithms. SECDU and SECDUF can output high-quality clustering results. SECDUF tunes parameters of clustering mode automatically and no man＇s action involves through the whole process. In addition, SECDUF has a high clustering performance.     

The refinement algorithm consideration in text clustering scheme based on multilevel graph

To construct a high efficient text clustering algorithm, the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed, The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application, The experiment result demonstrated that the muhilevel graph text clustering algorithm is available,     

Neuromuscular synaptogenesis: clustering of acetylcholine receptors revisited

Clustering of neurotransmitter receptors in the postsynaptic membrane is critical for efficient synaptic transmission. During neuromuscular synaptogenesis, clustering of acetylcholine receptors (AChRs) is an early sign of postsynaptic differentiation. Recent studies have revealed that the earliest AChR clusters can form in the muscle independent of motorneurons. Neurally released agrin, acting through the muscle-specific kinase MuSK and rapsyn, then causes further clustering and localization of clusters underneath the nerve terminal. AChRs themselves are required for agrin-induced clustering of several postsynaptic proteins, most notably rapsyn. Once formed, AChR clusters are stabilized by several tyrosine kinases and by components of the dystrophin/utrophin glycoprotein complex, some of which also direct postnatal synaptic maturation such as formation of postjunctional folds. This review summarizes these recent results about AChR clustering, which indicate that early clustering can occur in the absence of nerves, that AChRs play an active role in the clustering process and that partly different mechanisms direct formation versus stabilization of AChR clusters. Received 10 April 2002; received after revision 4 June 2002; accepted 10 June 2002     

一种新的混合聚类算法

聚类是数据挖掘的主要技术之一,是一种无导师监督的模式识别方式。聚类分析就是按照数据间的相似程度,依据特定的准则将数据划分成不同子类。K-平均算法是经典的聚类算法。蚂蚁聚类算法是近来涌现的新的聚类算法,它通过模拟蚁群的智能行为进行聚类分析,已经在数据挖掘中得到应用。通过分析蚂蚁聚类算法和K-平均算法两种不同聚类算法的基本思想,将两种算法结合得到混合聚类算法,仿真实验证明混合聚类算法的算法性能优于蚂蚁算法和K-平均算法。     

基于最邻近聚类和向量模糊c-均值的混沌预测

针对混沌时间序列难预测的问题,提出一种新的基于最邻近聚类和向量模糊c-均值(FCMV)聚类算法的模糊建模方法。其前提参数辨识分两步,首先用最近邻聚类法初始划分输入空间,得到规则数及初始聚类中心,再用FCMV把具有相同收敛向量的聚类中心归到同一个区域来优化前一步得到的聚类中心,得到前提参数;采用递推最小二乘算法辨识模型的结论参数。最后通过对Mackey-Glass混沌时间序列的建模和预测验证了该方法的有效性与实用性。