模糊数的四则运算性质及其线性方程

讨论模糊数的加、减、乘、除的运算性质，提出模糊数线性方程的概念，并给出这种方程的一种解法。     

幂模糊数方程和二次模糊方程的结构元求解方法

定义了幂模糊数和幂模糊数方程,基于结构元方法研究了幂模糊数运算和幂模糊数方程的求解,给出了隶属函数的表达式.同时,利用区间[-1,1]上的单调函数将二次模糊方程的求解问题转化为经典参数方程组的求解问题,给出了二次模糊方程解存在的充要条件,并辅以数值例子.     

用积分因子的思想和分部积分法求解常系数非齐次线性方程

用积分因子的思想和分部积分法求解了常系数非齐次线性方程,给出了一般的常系数非齐次线性方程的求解方法.     

求解非线性方程七阶收敛的牛顿迭代修正格式

本文研究了非线性方程求解的问题.利用泰勒公式和耦合方法,获得了一种求解非线性方程的加速收敛的七阶迭代改进格式,该格式不需要计算高阶导数,且具有更大的收敛半径,大大提高了计算效率.     

模糊数的相等、同一与等式限定运算

讨论了在模糊数运算中相等与同一的区别,在Klir的模糊数限定运算基础上提出了模糊数的等式限定运算以及等式限定运算的结构元表示方法,解决了传统模糊数运算的不可逆问题.通过模糊数的结构元表示方法,将其等式限定运算转换为两个同序单调函数的运算,这不仅仅给出等式限定运算的可操作形式,同时对于求解模糊数方程也给出了具体的计算方法.     

Goldstein线搜索下Levenberg-Marquardt方法的全局收敛性

给出在Goldstein线搜索条件下求解非线性方程的Levenberg-Marquardt方法, 在较为温和的条件下证明了该方法的全局收敛性, 并且利用该方法对广义互补问题进行了求解分析.     

关于非线性方程加速迭代的注记

对比了两类求解一元非线性方程迭代法的收敛速度 ,讨论了其异同点 ,并进行了算法时间复杂性分析 .     

综合型模糊线性规划分析

模糊线性规划问题是模糊数学规划的研究基础，已经有许多学在这一领域取得了卓有成效的研究成果。但这些研究都是针对特定类型的模糊线性规划开展的，而没有将模糊线性规划放在一般环境下进行综合考虑。本对模糊线性规划的一般模型进行了分析，提出了综合型模糊线性规划问题的求解方法。     

模糊数和模糊值函数的等式限定运算

通过模糊数的结构元表示方法,利用两个单调函数的自反单调变换构造了等式限定算子,推广了文[6]中的等式限定运算,处理了存在负模糊情况下关于乘法运算的不可逆问题.同时,本文还将等式限定运算推广到模糊值函数上,提出了模糊值函数等式限定运算的结构元方法,解决了模糊值函数运算的不可逆问题.     

模糊值函数的凸性问题

为了进一步研究模糊值函数的性质,基于模糊值函数结构元解析表述的有关理论,首先以广义模糊数序关系为前提,通过模糊不等式限定运算,研究了模糊值函数的凸性,并给出了凸模糊值函数的判定方法与性质.进一步通过结构序的排序方法,将模糊值函数的有关研究转换为对其伴随函数的研究,给出了模糊值函数伴随单调性,伴随凸性的定义,并研究了伴随凸模糊值函数的性质,以及伴随单调性和伴随凸性的判定方法.最后对单调性与伴随单调性,以及凸性与伴随凸性的关系进行了分析.     

n阶线性模糊微分方程的结构元解法

研究了n阶线性模糊微分方程的模糊初值问题,将n阶线性模糊微分方程转化成一阶线性模糊微分方程组,利用结构元方法将模糊线性微分方程组转化成两个分明的线性微分方程组,通过分明的线性微分方程组的解构造出原n阶线性模糊微分方程的解.最后,给出了具体的算例.     

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.     

Exact analytical inversion of TSK fuzzy systems with singleton and linear consequents

In literature, exact inversion methods for TSK fuzzy systems exist only for the systems with singleton consequents. These methods have binding limitations such as strong triangular partitioning, monotonic rule bases and/or invertibility check. These extra limitations lessen the modeling capabilities of the TSK fuzzy systems. In this study, an exact analytical inversion method for TSK fuzzy systems with singleton and linear consequents is presented. The only limitation of the proposed method is that the inversion variable should be represented by piecewise linear membership functions (PWL-MFs). In this case, the universe of discourse of the inversion variable is divided into specific regions in which only one linear piece exists for each PWL-MF at most. In the proposed method, the analytical formulation of TSK fuzzy system is expressed in terms of the inversion variable by using linear equations of PWL-MFs. Thus, the fuzzy system output in any region can be obtained by using the appropriate parameters of the linear equations of PWL-MFs defined within the related region. This expression provides a way to obtain linear and quadratic equations in terms of the inversion variable for TSK fuzzy systems with singleton and linear consequents, respectively. So, it becomes very easy to find exact inverse solutions for each region by using explicit analytical solutions for linear or quadratic equations. The proposed inversion method has been illustrated through simulation examples.     

一类区间数系数矩阵模糊线性方程组的迭代算法

讨论模糊线性方程组X=A-X+U解的存在条件及其迭代算法(其中,A-是区间数为元素的n阶矩阵,未知量X和常量U都是以模糊数为元素的n维向量,并且其加法和乘法均由Zadeh的扩张原理定义).首先研究解的存在条件,尔后探讨求解的迭代算法及误差估计.     

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.     

Solution and Application of the Matrix Equation AX~=b~

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Evaluation of fully fuzzy matrix equations by fuzzy neural network

In this paper, a new hybrid method based on fuzzy neural network for approximate solution of fully fuzzy matrix equations of the form AX=D$\mathit{AX}=D$, where A and D are two fuzzy number matrices and the unknown matrix X is a fuzzy number matrix, is presented. Then, we propose some definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy matrix equations and fuzzy neural network. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution of fuzzy matrix equations that supposedly has a unique fuzzy solution, a simple algorithm from the cost function of the fuzzy neural network is proposed. To illustrate the easy application of the proposed method, numerical examples are given and the obtained results are discussed.     

On the Optimal Three-tier Multimedia Streaming Services

In this paper, we provide a mathematical model for the streaming media provider seeking a minimum cost while fulfilled the requirements assumed by a three-tier framework. The proposed model involves a linear cost function subjected to fuzzy relational equations, where the algebraic operations employed are the max-min operations.     

A new method for solving fully fuzzy linear programming problems

Lotfi et al. [Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution, Appl. Math. Modell. 33 (2009) 3151–3156] pointed out that there is no method in literature for finding the fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems and proposed a new method to find the fuzzy optimal solution of FFLP problems with equality constraints. In this paper, a new method is proposed to find the fuzzy optimal solution of same type of fuzzy linear programming problems. It is easy to apply the proposed method compare to the existing method for solving the FFLP problems with equality constraints occurring in real life situations. To illustrate the proposed method numerical examples are solved and the obtained results are discussed.     

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.