乘积型模糊B-代数

引入乘积型模糊B-代数的概念,提供它们的几个例子,研究它们的一些性质.讨论模糊B-代数与乘积型模B-代数的关系,研究乘积型模糊B-代数的同态象与同态原象的性质,给出B-代数上乘积型模糊B-代数与B-代数的积代数上乘积型模糊B-代数的关系.     

模糊关系几何规划

提出一类目标函数为正项式,约束是模糊关系方程的优化问题。阐述模糊关系方程解集的结构以及求解的方法,基于目标函数中每个单项式的指数取值情况讨论最优解,并且给出解决此类优化问题的一个程序,为了说明该方法的有效性给出具体例子。     

约束条件为加法-取小模糊关系系统的带权min-max规划

定义了目标函数为带权重的min-max规划,约束条件为加法-取小模糊关系不等式系统,研究此模糊关系规划问题的求解思路和方法。当此模糊关系系统相容时,给出了一个求解此模糊关系规划问题的新算法和例子。     

模糊乘积的"失效"与"失真"的一个判定

给出模糊乘积"失真"的充要条件,并讨论模糊乘积的"失效"与"失真"的关系.     

模糊乘积的“失效”与“失真”的一个判定

给出模糊乘积"失真"的充要条件,并讨论模糊乘积的"失效"与"失真"的关系.     

最大乘积型Fuzzy文法与自动机

本文对提出的最大乘积型Ｆｕｚｚｙ文法与最大乘积型Ｆｕｚｚｙ自动机的关系作了详细讨论，得出了两个重要的结论。即：由给定的最大乘积型Ｆｕｚｚｙ正规文法可构造一个最大乘积型Ｆｕｚｚｙ自动机使得二者接受的语言集相同，反之亦然。从而达到自动识别语言的目的。     

模糊数无穷乘积

在定义了区间无穷乘积、模糊数无穷乘积及其收敛的基础上 ,利用 [3]中得到的关于模糊级数收敛的某些结论 ,得出模糊数无穷乘积与实数无穷乘积理论类似的结果 ,从而说明本文定义的模糊数无穷乘积确为实数无穷乘积的推广。     

目标系数模糊型模糊关系线性规划

提出了目标系数模糊型模糊关系线性规划问题,这是传统模糊关系线性规划的扩展.以三角模糊数为例,基于它的一种排序方法给出了求解该类规划的一个算法.最后,为了说明算法的有效性给出了两个数值例子.     

模糊数测度空间上模糊值函数的模糊值积分的Fubini定理

建立卡氏积空间 (X×Y,S× T)上的乘积区间值测度和乘积模糊值测度 ,证明模糊值测度空间上模糊值函数的模糊值积分的 Fubini定理。     

函数型部分线性乘积模型的B-样条估计

本文研究了函数型部分线性乘积模型,该模型可用于响应变量为正数的函数型数据的统计建模问题,经过对数变换后模型转化为函数型部分线性模型.基于B-样条,通过极小化最小一乘相对误差(LARE)和最小乘积相对误差(LPRE),分别给出模型的LARE估计和LPRE估计,其中B-样条基的维数利用Schwarz信息准则选取.对两种估计方法分别给出斜率函数估计的相合性和参数部分估计的渐近正态性,并且证明了斜率函数的收敛率达到了非参数函数估计的最优速率.蒙特卡洛模拟用来比较所提出的方法与最小一乘(LAD)估计和最小二乘(LS)估计在不同误差分布下的有限样本性质,模拟结果表明所提方法是有效和实用的.最后通过一个实际数据分析的例子来说明模型的应用.     

Minimizing a Linear Objective Function with Fuzzy Relation Equation Constraints

An minimization problem with a linear objective function subject to fuzzy relation equations using max-product composition has been considered by Loetamonphong and Fang. They first reduced the problem by exploring the special structure of the problem and then proposed a branch-and-bound method to solve this 0-1 integer programming problem. In this paper, we provide a necessary condition for an optimal solution of the minimization problems in terms of one maximum solution derived from the fuzzy relation equations. This necessary condition enables us to derive efficient procedures for solving such optimization problems. Numerical examples are provided to illustrate our procedures.     

A Note on Fuzzy Relation Programming Problems with Max-Strict-t-Norm Composition

The fuzzy relation programming problem is a minimization problem with a linear objective function subject to fuzzy relation equations using certain algebraic compositions. Previously, Guu and Wu considered a fuzzy relation programming problem with max-product composition and provided a necessary condition for an optimal solution in terms of the maximum solution derived from the fuzzy relation equations. To be more precise, for an optimal solution, each of its components is either 0 or the corresponding component's value of the maximum solution. In this paper, we extend this useful property for fuzzy relation programming problem with max-strict-t-norm composition and present it as a supplemental note of our previous work.     

Monomial geometric programming with fuzzy relation equation constraints

Monomials are widely used. They are basic structural units of geometric programming. In the process of optimization, many objective functions can be denoted by monomials. We can often see them in resource allocation and structure optimization and technology management, etc. Fuzzy relation equations are important elements of fuzzy mathematics, and they have recently been widely applied in fuzzy comprehensive evaluation and cybernetics. In view of the importance of monomial functions and fuzzy relation equations, we present a fuzzy relation geometric programming model with a monomial objective function subject to the fuzzy relation equation constraints, and develop an algorithm to find an optimal solution based on the structure of the solution set of fuzzy relation equations. Two numerical examples are given to verify the developed algorithm. Our numerical results show that the algorithm is feasible and effective.     

An Algorithm for Solving Optimization Problems with One Linear Objective Function and Finitely Many Constraints of Fuzzy Relation Inequalities

An optimization model with one linear objective function and fuzzy relation equation constraints was presented by Fang and Li (1999) as well as an efficient solution procedure was designed by them for solving such a problem. A more general case of the problem, an optimization model with one linear objective function and finitely many constraints of fuzzy relation inequalities, is investigated in this paper. A new approach for solving this problem is proposed based on a necessary condition of optimality given in the paper. Compared with the known methods, the proposed algorithm shrinks the searching region and hence obtains an optimal solution fast. For some special cases, the proposed algorithm reaches an optimal solution very fast since there is only one minimum solution in the shrunk searching region. At the end of the paper, two numerical examples are given to illustrate this difference between the proposed algorithm and the known ones.     

Geometric programming problem with single-term exponents subject to max-product fuzzy relational equations

In this paper, an optimization model for minimizing an objective function with single-term exponents subject to fuzzy relational equations specified in max-product composition is presented. The solution set of such a fuzzy relational equation is a non-convex set. First, we present some properties for the optimization problem under the assumptions of both negative and nonnegative exponents in the objective function. Second, an efficient procedure is developed to find an optimal solution without looking for all the potential minimal solutions and without using the value matrix. An example is provided to illustrate the procedure.     

The Constructions of the Max-min Fuzzy Relation Equations That Have a Unique Minimal Solution

ＴｈｅＣｏｎｓｔｒｕｃｔｉｏｎｓｏｆｔｈｅＭａｘｍｉｎＦｕｚｙＲｅｌａｔｉｏｎＥｑｕａｔｉｏｎｓＴｈａｔＨａｖｅａＵｎｉｑｕｅＭｉｎｉｍａｌＳｏｌｕｔｉｏｎＬｉＪｉａｎｘｉｎＨｕＧａｎｇ（ＴｅａｃｈｉｎｇａｎｄＲｅｓｅａｒｃｈＳｅｃｔｉｏｎｏｆＭ...     

A new algorithm for minimizing a linear objective function subject to a system of fuzzy relation equations with max-product composition

In this paper, we study a problem of minimizing a linear objective function subject to a system of fuzzy relation equations with max-product composition. First, we present a standard form of an original model. Then by using the concept of chained-set suite, we educe optimal solutions of the standard form, with which we can get optimal solutions of the original model. On basis of these results, finally we get an algorithm for the studied problem, which is simple and rapid for application.     

基于和积和最大积的信念传播算法的收敛性分析

在介绍马尔可夫网络和标准信念传播(Belief Propagation,BP)算法的基础上,以具有4个隐含结点的无环马尔可夫网络为例,详细分析了基于和积和最大积的BP算法收敛性,结果表明,在网络为无环马尔可夫网络时,基于和积的BP算法收敛于全局最优解,而基于最大积的BP算法在任何初值条件下,只要经过有限次迭代,BP算法必收敛到唯一固定点,若算法有唯一最优点,则此固定点为最优点.以实证分析为工具,研究BP算法收敛性,可作为BP算法基础理论研究的一次有益尝试.