基于核方法的模糊对象语言概念格聚类分析

在现实生活中,存在着大量语言值数据.为了解决在语言环境中不确定信息的聚类问题,本文提出了一种新的机器学习方法,即基于核方法的模糊对象语言概念格聚类分析模型.该模型通过融合层次聚类与概念格聚类的原理,在寻找到层次聚类局部最优层次的同时优化概念格聚类中的概念选择与概念构造问题.具体地,提出模糊对象语言概念格及其相关性质,它...     

基于随机采样模糊聚类和卡尔曼滤波方法的模糊辨识

提出一种新的基于模糊聚类和卡尔曼滤波方法的模糊辨识算法 .该方法是基于快速模糊聚类 ,计算给定样本在各类中的隶属度 ,并利用卡尔曼滤波方法辨识模糊模型的结论参数 .整个辨识过程与一般的模糊聚类方法 [1 ]相比 ,需要的 CPU时间大大缩短 .最后通过仿真实例验证了该方法的有效性 .     

基于Dirichlet分布有限混合模型的Bayes聚类

本文基于Dirichlet分布有限混合模型,提出了一种用于成分数据的Bayes聚类方法.采用EM算法获得模型参数的估计,用BIC准则确定类数,用类似于Bayes判别的方法对各观测分类.推导了计算公式,编写出程序.模拟研究结果表明,本文提出的方法有较好的聚类效果.     

模糊聚类与模糊识别理论模型研究

指出文 [1]模糊聚类与模糊识别模型的局限性 ,进一步研究模糊环境下的目标函数 ,修改模糊聚类中心的计算公式 ,提出计算权重的一个公式 ,拓广模糊聚类与模糊识别理论模型的应用范围     

基于隐变量的有限混合模型对有序数据的Bayes聚类分析

本文基于隐变量的有限混合模型,提出了一种用于有序数据的Bayes聚类方法.我们采用EM算法获得模型参数的估计,用BIC准则确定类数,用类似于Bayes判别的方法对各观测分类.模拟研究结果表明,本文提出的方法有较好的聚类效果,对于中等规模的数据集,计算量是可以接受的.     

概念粒计算系统外延的判别定理

概念粒计算系统是基于两个完备格之间的外延内涵算子和内涵外延算子构成的模型系统,它包括经典概念格,L模糊概念格及变精度概念格等.本文以三种概念粒计算系统为模型研究了概念外延的特征及其相互关系,给出了外延为经典集、内涵为模糊集和外延为模糊集、内涵为经典集这两种概念粒计算系统的概念外延判别定理,并且讨论了几种模型概念之间的关系与性质.     

变量的有限混合模型对有序数据的Bayes聚类分析

本文基于隐变量的有限混合模型, 提出了一种用于有序数据的Bayes聚类方法\bd 我们采用EM算法获得模型参数的估计, 用BIC准则确定类数, 用类似于Bayes判别的方法对各观测分类\bd 模拟研究结果表明, 本文提出的方法有较好的聚类效果, 对于中等规模的数据集, 计算量是可以接受的.     

基于矩阵变换的聚类集成优化模型

聚类集成方法能够有效综合不同的聚类结果,提高聚类的精确度和稳定性.提出了一个基于矩阵变换的聚类集成优化模型,模型通过矩阵变换代替传统方法中的聚类配准模式,使得优化模型更加简洁,然后给出了求解该优化模型的叠代算法.实验表明,提出的聚类集成方法能够有效提高聚类集成的稳定性和精确度,并且在聚类数目比较少时,算法有着较低的时间复杂度.     

基于Shifted Legendre多项式非线性年龄结构种群模型的数值解

基于Shifted Legendre多项式研究非线性年龄结构种群模型的数值解问题.定义了在区间[0,A]×[0,T]上函数的Shifted Legendre逼近多项式,通过Shifted Legendre算子矩阵结合Tau方法,把求解非线性年龄结构种群模型的数值解问题转化成非线性代数方程的求解问题.数值算例的结果显示该算法有效.     

符号函数的Legendre非线性逼近

1引言许多数学和物理工作者研究了逼近形式正交多项式级数的具有较好收敛性的非线性方法,如文献[2-5,9]．这些非线性逼近方法的一个共同点是使用了线性级数中正交多项式的母函数．众所周知,的符号函数具有很多的应用,如文献[7]利用符号函数的积分表示来分析相联存储器的回想过程．文献[1]及其中所引用的一些文献为了获得交迭格Dirac算子,讨论了符号函数的有理逼近和连分式展开．在本文中,我们研究符号函数的Lengendre     

一反应扩散模型解的有限元方法和误差分析

１引言在地下水运移过程中,污染物（溶质）随地下水在含水层中运移,并常常发生各种化学反应．文献［１－３］等提出并论述了三种化学物质（如Ｍ１,Ｍ２和Ｍ３）之间发生的一类化学反应．文献［４,５］等建立和描述了这类反应的数学模型（Ｐ）．文献［６,７」的作者首次对模型（Ｐ）进行了理论上的定性分析,主要是利用上,下解方法,算子半群理论和Ｓｏｂｏｌｅｖ空间的般人定理等论证了模型（Ｐ）的整体古典解的存在唯一性和渐近性质,文［６,７］也讨论了整体解的极限性态和收敛性估计．此外,文［８,９］等也就模型（Ｐ）的一类特…     

The method of exact diagonalization preserving the total spin and taking the point symmetry of the two-dimensional isotropic Heisenberg magnet into account

We propose a double-pass method of exact diagonalization of a finite cluster on the base of functions that have a definite total spin and transform by a definite irreducible representation of the point symmetry group of the lattice. We also propose the method for approximating the energy spectrum in the thermodynamic limit using the spectrum of the surrounding states, which increases the calculation accuracy leaving the cluster size invariant. The algorithm details are extensively illustrated with an example of clusters of spin 1/2 on a simple square lattice. __________ Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 149, No. 2, pp. 262–280, November, 2006.     

Bias expansion of spatial statistics and approximation of differenced lattice point counts

Investigations of spatial statistics, computed from lattice data in the plane, can lead to a special lattice point counting problem. The statistical goal is to expand the asymptotic expectation or large-sample bias of certain spatial covariance estimators, where this bias typically depends on the shape of a spatial sampling region. In particular, such bias expansions often require approximating a difference between two lattice point counts, where the counts correspond to a set of increasing domain (i.e., the sampling region) and an intersection of this set with a vector translate of itself. Non-trivially, the approximation error needs to be of smaller order than the spatial region’s perimeter length. For all convex regions in 2-dimensional Euclidean space and certain unions of convex sets, we show that a difference in areas can approximate a difference in lattice point counts to this required accuracy, even though area can poorly measure the lattice point count of any single set involved in the difference. When investigating large-sample properties of spatial estimators, this approximation result facilitates direct calculation of limiting bias, because, unlike counts, differences in areas are often tractable to compute even with non-rectangular regions. We illustrate the counting approximations with two statistical examples.     

On the Characteristic of Proper Lattice Implication Algebras

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??Hidden Markov model is widely used in statistical modeling of time, space and state transition data. The definition of hidden Markov multivariate normal distribution is given. The principle of using cluster analysis to determine the hidden state of observed variables is introduced. The maximum likelihood estimator of the unknown parameters in the model is derived. The simulated observation data set is used to test the estimation effect and stability of the method. The characteristic is simple classical statistical inference such as cluster analysis and maximum likelihood estimation. The method solves the parameter estimation problem of complex statistical models.     

Maximum Likelihood Estimation of Hidden Markov Multivariate Normal Distribution Parameters

Hidden Markov model is widely used in statistical modeling of time, space and state transition data. The definition of hidden Markov multivariate normal distribution is given. The principle of using cluster analysis to determine the hidden state of observed variables is introduced. The maximum likelihood estimator of the unknown parameters in the model is derived. The simulated observation data set is used to test the estimation effect and stability of the method. The characteristic is simple classical statistical inference such as cluster analysis and maximum likelihood estimation. The method solves the parameter estimation problem of complex statistical models.     

二阶离散隐马尔科夫模型的严格定义及等价性质

隐马氏模型作为一种具有双重随机过程的统计模型,具有可靠的概率统计理论基础和强有力的数学结构,已被广泛应用于语音识别、生物序列分析、金融数据分析等领域.由于传统的一阶隐马氏模型无法表示更远状态距离间的依赖关系,就可能会忽略很多有用的统计特征,故有人提出二阶隐马氏模型的概念,但此概念并不严格.本文给出二阶离散隐马尔科夫模型的严格定义,并研究了二阶离散隐马尔科夫模型的两个等价性质.     

个体风险模型的Poisson复合模型近似

本文首先简要介绍了停止损失序在风险期望值相等时的一些推论 ,然后借助停止损失序详尽地讨论了仅取有限个值的随机变量的格点化 ,并以此作为出发点在多重衰减模型的框架下 ,给出了以 Poisson复合模型近似个体风险模型的一般步骤 ,同时导出了评价这一近似优劣程度的一个数量指标的解析表达式。     

多目标minimax问题的极大熵逼近收敛性

本文利用极大熵逼近函数，展开了多目标ｍｉｎｉｍａｘ问题的逼近方法的研究，并讨论了该逼近方法的收敛性，所得结果是目前已有的结果进一步拓广．     

Axiomatic characterizations of dual concept lattices

Formal concept analysis is an algebraic model based on a Galois connection. It is used for symbolic knowledge exploration from an elementary form of a formal context. This paper mainly presents a general framework for concept lattice in which axiomatic approaches are used. The relationship between concept lattice and dual concept lattice is first studied. Based on set-theoretic operators, generalized concept systems are established. And properties of them are examined. By using axiomatic approaches, a pair of dual concept lattices is characterized by different sets of axioms. The connections between 0-1 binary relations and generalized concept systems are examined. And generalized dual concept systems can be constructed by a pair of dual set-theoretic operators. Axiomatic characterizations of the generalized concept systems guarantee the existence of a binary relation producing a formal context.