一种改进的模糊C均值聚类算法

针对经典的C均值聚类算法以及模糊C均值聚类算法所存在的两个方面的问题：一是算法对初始聚类中心的过分依赖性,通常的聚类算法往往对于不同的初始聚类中心会得到不同的聚类结果;二是算法需要预先知道实际的聚类数目,而在实际应用中,聚类数目却是未知的。基于此提出了模糊C均值聚类算法的一种改进算法,即在标准的模糊C均值聚类算法的基础上,给目标函数加入了一个惩罚项,使得上述问题得以解决。并通过仿真实验证实了新算法的可行性和有效性。     

模糊蚁群聚类及其在数据挖掘中的应用

提出了一种基于蚁群算法的动态模糊聚类分析方法,并研究其在数据挖掘中的应用。该算法首先利用蚁群算法的较强处理局部极值的能力,克服了FCM算法对初始化的敏感,动态的确定了聚类数目和中心;然后利用蚁群聚类得到的结果,再进行FCM聚类弥补蚁群算法的不足。从而将蚁群算法与模糊C均值聚类有机的结合,实现了基于改进的目标函数聚类分析,可以寻求到具有全局分布特性的最优聚类。     

基于有效性测度的基因表达数据的模糊聚类分析

本文讨论了模糊聚类中的模糊C均值算法和聚类有效性测度。结合基因微阵列的特点，设计并实现了一种基于聚类有效性函数的模糊C均值模型。将该种模型运用于公开的白血病基因表达数据，取得了与实际情况相吻合的实验结果。     

一种快速的模糊C均值聚类彩色图像分割方法

FCM用于彩色图像分割存在聚类数目需要事先确定、计算速度慢的问题,为此,提出一种快速的模糊C均值聚类方法（FFCM）。首先,对原始彩色图像进行基于梯度图的分水岭变换,从而把原始彩色图像数据分成一些具有色彩一致性的子集;然后,利用这些子集的大小和中心点进行模糊聚类。由于FFCM聚类样本数量显著减小,因此可以大幅提高模糊C均值聚类算法的计算速度,进而可以采用聚类有效性指标确定聚类数目。实验表明,这种方法不需要事先确定聚类数目,在聚类有效性能不变的前提下,可以使模糊聚类的速度得到明显提高,实现了彩色图像的快速分割。     

基于聚类有效性分析的模糊粗糙集归纳学习方法

引入模糊C均值聚类算法进行连续属性模糊化,通过聚类有效性分析来确定最佳分类数目,克服了属性模糊化方法需要人为确定划分类数的缺点。用属性模糊化得到的属性隶属度矩阵约简模糊粗糙属性,由此提出一种基于模糊粗糙集的属性约简算法。实例验证了该方法的可行性和有效性。     

快速模糊C均值聚类彩色图像分割方法

模糊C均值(FCM)聚类用于彩色图像分割具有简单直观、易于实现的特点，但存在聚类性能受中心点初始化影响且计算量大等问题，为此，提出了一种快速模糊聚类方法(FFCM)。这种方法利用分层减法聚类把图像数据分成一定数量的色彩相近的子集，一方面，子集中心用于初始化聚类中心点；另一方面，利用子集中心点和分布密度进行模糊聚类，由于聚类样本数量显著减少以及分层减法聚类计算量小，故可以大幅提高模糊C均值算法的计算速度，进而可以利用聚类有效性分析指标快速确定聚类数目。实验表明，这种方法不需事先确定聚类数目并且在优化聚类性能不变的前提下，可以使模糊聚类的速度得到明显提高，实现彩色图像的快速分割。     

改进的粗糙模糊和模糊粗糙K-均值聚类算法

在分析归纳原有聚类方法不足的基础上,结合粗糙理论和模糊理论,给出了改进的粗糙模糊K-均值聚类算法;设计了新的模糊粗糙K-均值聚类算法,并验证了该聚类算法的有效性;进而将这两种聚类算法应用到支持向量机中,对训练样本做预处理,以减少样本数目,提高了其训练速度和分类精度。     

基于模糊聚类的图像分割方法

本文分析了模糊聚类在图像分割领域的应用,介绍了模糊集和聚类分析的作用,最后引出了模糊C均值聚类图像分割算法。     

基于改进型模糊聚类的模糊系统建模方法

结合减法聚类和模糊C均值聚类，提出了一种改进型聚类算法，加快了收敛速度．利用改进后的算法对模糊系统输入或输出的样本集聚类，对聚类结果采用Trust-Region法拟合高斯型和S型函数，以实现模糊系统输入、输出空间的划分和隶属度函数参数的确定．结合MATLAB的模糊和曲线拟合工具箱，详述了如何在标准算法上进行改进和模糊系统建模．通过对IRIS标准数据聚类实验以及在解决机械加工误差复映问题上的应用，验证了改进后算法和建模方法的有效性．     

一种改进的模糊C均值算法在入侵检测中的应用

为解决模糊C均值(FCM)聚类算法在入侵检测中存在的检测效率低的问题,提出一种改进方法,将改进的模糊C均值聚类算法应用于入侵检测。测试表明,该算法有效提高了聚类检测的检测率,降低了误检测率,具有可行性和有效性。     

Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP

Failure mode and effects analysis (FMEA) is a widely used engineering technique for designing, identifying and eliminating known and/or potential failures, problems, errors and so on from system, design, process, and/or service before they reach the customer (Stamatis, 1995). In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O), and detectability (D) are evaluated and a risk priority number (RPN) is obtained by multiplying these factors. There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. In this study a fuzzy approach, allowing experts to use linguistic variables for determining S, O, and D, is considered for FMEA by applying fuzzy ‘technique for order preference by similarity to ideal solution’ (TOPSIS) integrated with fuzzy ‘analytical hierarchy process’ (AHP). The hypothetical case study demonstrated the applicability of the model in FMEA under fuzzy environment.     

Node connectivity and arc connectivity of a fuzzy graph

The fuzzy graph approach is more powerful in cluster analysis than the usual graph - theoretic approach due to its ability to handle the strengths of arcs effectively. The concept of node-strength sequence is introduced and is studied in a complete fuzzy graph. Two new connectivity parameters in fuzzy graphs namely, fuzzy node connectivity (κ) and fuzzy arc connectivity (κ^′) are introduced and obtained the fuzzy analogue of Whitney’s theorem. Fuzzy node cut, fuzzy arc cut and fuzzy bond are defined. Fuzzy bond is a special type of a fuzzy bridge. It is proved that at least one of the end nodes of a fuzzy bond is a fuzzy cutnode. It is shown that κ=κ^′ for a fuzzy tree and it is the minimum of the strengths of its strong arcs. The relationships of the new parameters with already existing vertex and edge connectivity parameters are studied and is shown that the value of all these parameters are equal in a compete fuzzy graph. Also a new clustering technique based on fuzzy arc connectivity is introduced.     

A generalized model of fuzzy rough sets

The consideration of approximation problem of fuzzy sets in fuzzy information systems results in theory of fuzzy rough sets. This paper focuses on models of generalized fuzzy rough sets, a generalized model of fuzzy rough sets based on general fuzzy relations are studied, properties and algebraic characterization of the model are revealed, and relationships between this model and related models are also discussed.     

An alternative characterization of fuzzy complement functional

In this short note we propose a novel fuzzy complement functional. This functional is different from other functionals known in the literature. However, it turns out to be an alternative characterization of the well-known negation function. We sincerely thank the anonymous reviewer whose insightful suggestions have significantly improved the paper.     

模糊数学在企业信息处理中的应用--模糊人事评价及人事安排、岗位培训决策支持系统

本文讨论了模糊数学在企业信息处理中的应用。提出了模糊人事评价体系，以及企业人事安排、企业员工技能培训等方面的模糊决策支持数学模型。     

基于模糊算法的数据库查询工具的设计

利用SQL进行数据库查询,可以完成复杂条件的数据查询,但是,它只能表示和处理精确数据,无法表达自然语言中的模糊。针对具体人员信息数据库,提出了基于人员年龄、身高与体重的模糊单词、模糊隶属函数、模糊算子的表达式,并提出了模糊SQL语句的自动生成方法。提出并实现的基于模糊算法的人员信息数据库查询工龄,已经应用于暂住人口IC卡管理信息系统,并对其它相关模糊数据库查询具有有较好的参考意义。     

An introduction to vague complemented ordered sets

The purpose of this paper is to introduce a theory of fuzzily defined complement operations on nonempty sets equipped with fuzzily defined ordering relations. Many-valued equivalence relation-based fuzzy ordering relations (also called vague ordering relations) provide a powerful and a comprehensive mathematical modelling of fuzzily defined partial ordering relations. For this reason, starting with a nonempty set X equipped with a many-valued equivalence relation and a vague ordering relation, a fuzzily defined complement operation (called a vague complement operation) on X will be formulated by means of the underling many-valued equivalence relation and vague ordering relation. Because of the fact that the practical implementations of vague complement operations basically depend on their representation properties, a considerable part of this paper is devoted to the representations of vague complement operations. In addition to this, the present paper provides various nontrivial examples for vague complements, and introduces a many-valued logical interpretation of quantum logic as a real application of vague complements.     

冲击式钻机的微机模糊控制

以往的冲击式钻井是凭人的感觉来判断钻具在井下的工作状况,容易失误而引发事故。所研制的微机控制冲击式钻机操作指导系统利用模糊控制原理,能对钻具的正常、绳松和空顿三种状态作出及时准确的判断,并指导司钻作相应的提绳和给进操作。还能对断钻头、断绳和卡钻三种故障进行检测和报警。     

Rough Sets Model Based on Random Fuzzy Sets and Fuzzy Logic Operators

As knowledge block in knowledge base is fuzzy and obtained randomly, we propose a random fuzzy rough set model based on random fuzzy sets and fuzzy logic operators. We give some properties of the random fuzzy rough set. We investigate the relationship between fuzzy measures defined by lower approximation and upper approximation of fuzzy set and fuzzy probability measures.