基于粗集与概念格的属性简约讨论

概念格的属性简约是在形式背景下解决复杂问题的重要途径,通过对概念格、粗糙集的讨论,将两者有效结合,并借助粗糙集上(下)近似的方法,得出了一个对概念格属性简约的方法,方法将二维的概念格属性简约转化为一维的一种对象格的简约,避免了形式背景下的概念的计算和进一步的可辨识矩阵的计算,方法简便,算法简单易实现,是概念格属性简约有效的算法.     

完全分配格上的覆盖粗糙集模型

将集合论中的覆盖概念抽象到完全分配格L上,利用它定义格L上关于覆盖的上(下)近似算子,给出格L上覆盖粗糙集模型.文中先讨论格L上覆盖的相关性质,进而研究了覆盖上(下)近似算子的性质,得到若干结果.     

多粒度粗糙近似算子的格结构研究

多粒度粗糙集模型是经典粗糙集模型的一种重要扩展形式,在多粒度粗糙集模型概念中,有乐观和悲观两种形式。本文先把Pawlak粗糙近似算子的格结构拓展到基于一般二元关系的粗糙近似算子的格结构,然后进一步拓展到基于一般二元关系的多粒度粗糙近似算子的格结构,主要给出基于一般二元关系的悲观多粒度粗糙近似算子的格结构。     

基于形式概念的粗糙近似

证明了对象(属性)概念的外延(内涵)是构成概念外延(内涵)的基本单位,由此提供了通过外延(内涵)基本元来构造形式概念的方法.定义了几种基于外延基本元和内涵基本元的上、下近似,给出了近似算子的性质,并讨论了这些近似算子之间的相互关系.     

基于粗糙集方法的概念格理论研究综述

概念格与粗糙集理论是软计算领域的两种不同方法,它们都在数据挖掘、知识工程、信息检索、人工智能、系统分析与管理决策等领域有重要应用。在介绍概念格基本概念的基础上,对近年来借助粗糙集方法研究概念格的粗糙集近似扩充、概念格的约简理论与方法、变精度概念格及基于概念格的模糊推理、概念粒计算系统的数学模型及迭代算法等方面进行了综述,并提出了进一步研究的问题和方向。     

The granular accuracy of approximation for the rough sets

自Pawlak提出粗糙集概念以来,人们一直对粗糙集的近似精度很有兴趣,出现了不少有关近似精度的文献.本文提出了粗糙集的粒度近似精度,讨论了粒度近似精度的性质,并与Pawlak近似精度和基于等价关系图过剩熵的近似精度进行了比较.比较发现粒度近似精度更具合理性.     

由子基生成的内部算子和闭包算子

本文研究粗糙集与拓扑空间的关系,统一地使用拓扑空间中的集合关于子基的内部和闭包来研究粗糙集理论和覆盖广义粗糙集理论中的下近似集和上近似集,以及由它们导出的关于子基的开集,导集,闭集,边界．研究这两个概念及由它们导出的相关概念的性质不仅对于粗糙集理论,而且对于拓扑学本身都有重要的理论和实际应用意义．     

近似空间与信任结构之间的关系

回顾了由二元关系产生的粗糙近似空间及其导出的各种粗糙近似算子的构造性定义,介绍了经典和模糊环境下各种信任结构及其导出的信任函数与似然函数的概念,给出了粗糙集理论中近似空间及其导出的下近似算子与上近似算子和证据理论中的信任结构及其导出信任函数与似然函数之间的相互关系及其应用背景。     

基于区间值模糊剩余蕴含算子的广义区间值模糊粗糙集模型

把粗糙集理论和区间值模糊集理论结合起来,利用粗糙集理论的构造性方法,提出了一种广义区间值模糊粗糙集理论模型。首先,利用区间值模糊剩余蕴含算子和它的对偶算子,定义了一种广义上下区间值模糊粗糙集近似算子。然后,利用该蕴含算子的性质,讨论了该模型上下近似算子的一系列性质。最后,确立了一些特殊的区间值模糊关系和区间值模糊粗糙集近似算子的联系。     

因素空间反馈外延包络及其改善

研究了因素空间中概念外延的两种近似方法,即反馈外延外包络和反馈外延内包络。首先从概念及其对立概念出发定义了这两种外延近似,讨论了其与粗糙集的上、下近似的关系,得出它们之间具有一致性的结论,并以此给出了两种包络之间的相关性质。随后,讨论了反馈外延包络对概念外延逼近精度问题,给出了四种改善方法。     

Images and preimages of subobjects under the morphisms in a new category of fuzzy sets-I

This paper is the third in a sequence of papers on categories by the same authors. In one of the papers, a new category of fuzzy sets was defined and a few results were established pertaining to that special category of fuzzy sets S. Here, the concept of a fuzzy subset of a fuzzy set is defined under the category S. Besides, the notions of images and preimages of fuzzy sets are also defined under morphisms in the category of fuzzy sets and how smoothly these images and preimages behave under the action of these morphisms is analyzed. Finally, results have been proved on algebra of morphisms of this category S.     

Greedy-Type Approximation in Banach Spaces and Applications

We continue to study the efficiency of approximation and convergence of greedy-type algorithms in uniformly smooth Banach spaces. Two greedy-type approximation methods, the Weak Chebyshev Greedy Algorithm (WCGA) and the Weak Relaxed Greedy Algorithm (WRGA), have been introduced and studied in [24]. These methods (WCGA and WRGA) are very general approximation methods that work well in an arbitrary uniformly smooth Banach space $X$ for any dictionary ${\Cal D}$. It turns out that these general approximation methods are also very good for specific dictionaries. It has been observed in [7] that the WCGA and WRGA provide constructive methods in $m$-term trigonometric approximation in $L_p$, $p\in[2,\infty)$, which realize an optimal rate of $m$-term approximation for different function classes. In [25] the WCGA and WRGA have been used in constructing deterministic cubature formulas for a wide variety of function classes with error estimates similar to those for the Monte Carlo Method. The WCGA and WRGA can be considered as a constructive deterministic alternative to (or substitute for) some powerful probabilistic methods. This observation encourages us to continue a thorough study of the WCGA and WRGA. In this paper we study modifications of the WCGA and WRGA that are motivated by numerical applications. In these modifications we are able to perform steps of the WCGA (or WRGA) approximately with some controlled errors. We prove that the modified versions of the {\it WCGA and WRGA perform as well as the WCGA and WRGA}. We give two applications of greedy-type algorithms. First, we use them to provide a constructive proof of optimal estimates for best $m$-term trigonometric approximation in the uniform norm. Second, we use them to construct deterministic sets of points $\{\xi^1,\ldots,\xi^m\} \subset [0,1]^d$ with the $L_p$ discrepancy less than $Cp^{1/2}m^{-1/2}$, $C$ is an effective absolute constant.     

Optimization with Variable Sets of Constraints and an Application to Truss Design

We discuss the minimization of a continuous function on a subset of Rⁿ subject to a finite set of continuous constraints. At each point, a given set-valued map determines the subset of constraints considered at this point. Such problems arise e.g. in the design of engineering structures.After a brief discussion on the existence of solutions, the numerical treatment of the problem is considered. It is briefly motivated why standard approaches generally fail. A method is proposed approximating the original problem by a standard one depending on a parameter. It is proved that by choosing this parameter large enough, each solution to the approximating problem is a solution to the original one. In many applications, an upper bound for this parameter can be computed, thus yielding the equivalence of the original problem to a standard optimization problem.The proposed method is applied to the problem of optimally designing a loaded truss subject to local buckling conditions. To our knowledge this problem has not been solved before. A numerical example of reasonable size shows the proposed methodology to work well.     

Numerical solution of the Dirichlet problem for nonlinear parabolic equations by a probabilistic approach

A number of new layer methods for solving the Dirichlet problemfor semilinear parabolic equations are constructed by usingprobabilistic representations of their solutions. The methodsexploit the ideas of weak sense numerical integration of stochasticdifferential equations in a bounded domain. Despite their probabilisticnature these methods are nevertheless deterministic. Some convergencetheorems are proved. Numerical tests are presented.     

Unexpectedly low angular extent of journal bearing pressures: experiment and theory

Journal bearings have been studied for a long time. Pressure solutions for the same, as presented in textbooks, typically have angular extents exceeding 150°. Here, for a bearing with a relatively larger clearance ratio (0.01 as opposed to, say, 0.001), our experiments show an angular extent of about 50° only. Such small angular extents cannot be predicted, even approximately, by the existing simple theories for journal bearing pressures. However, such theories are based on assumptions whereby only the relative speed between bearing and journal surfaces enters the governing equations. We discuss how these same assumptions motivate some new combinations of boundary conditions that allow reasonably simple numerical treatment. In this paper, the resulting families of possible solutions are computed semi-numerically using a Fourier series expansion in one direction and finite differences and numerical continuation in the other. We find that one such solution family contains small-extent solutions similar to those observed experimentally.     

Finite difference approximations for the fractional Fokker–Planck equation

The fractional Fokker–Planck equation has been used in many physical transport problems which take place under the influence of an external force field. In this paper we examine some practical numerical methods to solve a class of initial-boundary value problems for the fractional Fokker–Planck equation on a finite domain. The solvability, stability, consistency, and convergence of these methods are discussed. Their stability is proved by the energy method. Two numerical examples are also presented to evaluate these finite difference methods against the exact analytical solutions.     

Numerical solution of stochastic differential equations by second order Runge–Kutta methods

In this paper we propose the numerical solutions of stochastic initial value problems via random Runge–Kutta methods of the second order and mean square convergence of these methods is proved. A random mean value theorem is required and established. The concept of mean square modulus of continuity is also introduced. Expectation and variance of the approximating process are computed. Numerical examples show that the approximate solutions have a good degree of accuracy.     

A Birkhoff Theorem for Doubly Stochastic Matrices with Vector Entries

The concept of a doubly stochastic matrix whose entries come from a convex subset of the unit square is defined. It is proved that the only convex subsets of the unit square which contain (0,0) and (1, 1) and allow an extension of Birkhoff's characterization of the extreme points of the set of doubly stochastic matrices are parallelograms. A sufficient condition is given for a matrix to be extreme when the convex subset is not a parallelogram.     

A probabilistic approach to the solution of the Neumann problem for nonlinear parabolic equations

A number of new layer methods for solving the Neumann problemfor semilinear parabolic equations are constructed by usingprobabilistic representations of their solutions. The methodsexploit the ideas of weak-sense numerical integration of stochasticdifferential equations in a bounded domain. In spite of theprobabilistic nature these methods are nevertheless deterministic.Some convergence theorems are proved. Numerical tests on theBurgers equation are presented.     

The rate of approximation of Gaussian radial basis neural networks in continuous function space

There have been many studies on the dense theorem of approximation by radial basis feedforword neural networks, and some approximation problems by Gaussian radial basis feedforward neural networks(GRBFNs)in some special function space have also been investigated. This paper considers the approximation by the GRBFNs in continuous function space. It is proved that the rate of approximation by GRNFNs with n~d neurons to any continuous function f defined on a compact subset K(R~d)can be controlled by ω(f, n~(-1/2)), where ω(f, t)is the modulus of continuity of the function f .