基于∨-·运算的Fuzzy矩阵方程的摄动问题

定义基于∨ - .运算的 Fuzzy矩阵 A的同解简化矩阵 ,利用 A的同解简化矩阵 A(1) 给出基于∨ - .运算的 Fuzzy矩阵方程的矩阵解法 ,并研究这类 Fuzzy矩阵方程的摄动问题。     

关于Fuzzy矩阵的广义逆

本文分别给出了Fuzzy矩阵存在广义{1,3}-逆、广义{1,4}-逆以及Moore-Penrose广义逆Fuzzy矩阵的一些充要条件。又得到求上述广义逆Fuzzy矩阵的一些公式。主要的结果有: 1.Fuzzy矩阵A的广义{1,3}-逆A~((1.3))(广义{1,4}-逆A~((1.4))存在的充要条件是Fuzzy关系方程有解。2.Fuzzy矩阵A的Moore-Penrose广义逆A~T存在的充要条件是Fuzzy关系方程均有解。3.如果B、C分别为Fuzzy关系方程的一个解,那么。     

关于Fuzzy矩阵的广州逆

本文分别给出了Fuzzy矩阵存在广义{1，3}－逆、广义{1，4}－逆以及Mocre-Penrose广义逆Fuzzy矩阵的一些充要条件。又得到求上述广义逆Fuzzy矩阵的一些公式。主要的结果有：1．Fuzzy矩阵A的广义{1，3}－逆A^(1,3)（广义{1，4}－逆A^(1,4)的充要条件是Fuzzy关系方程X。A^T。A＝（A。A^T。Y＝A）有解。2．Fuzzy矩阵A的Mocre-Penrose广义逆A^＋存在的充要条件是Fuzzy关系方程X。A^T。A＝A，A。A^T。Y＝A均有解。3．如果B、C分别的Fuzzy关系方程X。A^T。A＝A，A。A^T。Y＝A的一个解，那么A^ ＝A^T。C。B＝C^T。AB^T＝C^T。B。A^T。     

Fuzzy矩阵方程=■的解及性质

本文首先给出了矩阵方程Ax＝b的解的定义，然后对此解进行了深入的研究。给出了锥形Fuzzy集的概念，讨论了方程Ax－b与锥形Fuzzy集之间的关系。最后证明了一类锥形Fuzzy集全体构成完备的Fuzzy度量空间。     

关于求解Fuzzy矩阵方程的注记

定义了 Fuzzy矩阵 A的同解简化矩阵 A( 2 ) ,利用同解简化矩阵 A( 2 ) 给出了 Fuzzy矩阵方程的简化解法 ,指出了文 [4]中定理 3的错误 .     

本征Fuzzy方程的一个通用算法

一、基本方程当 L=[0,1],“∧”、“∨”分别为取“Min”和“Max”运算时,(L,≤,∧,∨)是一个完全分配格,定义在此格上的n×n矩阵A称为Fuzzy矩阵。方程AoX=X称为Fuzzy矩阵A的本征方程,其中方程的解X为n×1 Fuzzy列向量,称为A的本征Fuzzy向量或A的本征元,将A的本征Fuzzy方程写成分量形式,即为n个Fuzzy方程:     

线性Fuzzy方程组(A~)(x~)=(b~)的解

重点研究线性Fuzzy方程组Ax=b的解，其中矩阵A和向量5均以有限Fuzzy数为其元素。文中首先指出，使用扩展原理和Fuzzy数运算规则有时会导致Ax=b没有解。本文以实广义逆矩阵为工具，给出了Ax=b的六个解，证明了它们都是R^n上的Fuzzy向量。     

完备Brouwerian格上Fuzzy关系方程有极小解的条件

本文在有限论域上对完备Brouwerian格上Fuzzy关系方程极小的存在问题作了探讨，首先构造了Fuzzy关系方程有解但无极小解的一个例子，然后在解集非空时给出了对Fuzzy关系方程的每一个解都存在一个小于等于它的极小解的一个充分条件及一个充要条件，特别地，在充分条件下给出了一类Fuzzy关系方程所有极小解的个数的公式。     

Fuzzy相似矩阵方程

文献[1]提出了变次Fuzzy相似矩阵方程X^k。H＝S的概念，并且指出它有解的充要条件是方程Y。H＝S有解，且其解中含有1型阵。本文将给出方程Y。H＝S有解且其解中含有1型阵的充要条件，而完满地解决了变次Fuzzy相似矩阵方程的解的存在性问题。     

一个关于Fuzzy关系方程是否有解的等价判别定理

Fuzzy关系方程已经广泛地应用于Fuzzy综合评判、Fuzzy控制等领域。本文通过直接比较Fuzzy关系方程的系数与常数的大小,给出一个判别所给的Fuzzy关系方程是否有解的行之有效的简便方法,并且在有解时还给出求其最大解、极小解及解集的方法。     

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.     

Subinverses of fuzzy matrices

A matrix operation is examined for fuzzy matrices and interesting properties of fuzzy matrices are obtained using the operation. Particularly some properties concerning subinverses and regularity of fuzzy matrices are given and the largest subinverse is shown by the properties. The properties are closely related to inverses of fuzzy matrices and fuzzy equations. Moreover fuzzy preorders are examined using the matrix operation and basic properties are obtained. The results are considered to be useful for the theory of fuzzy matrices.     

On regularity of block triangular fuzzy matrices

Necessary and sufficient conditions are given for the regularity of block triangular fuzzy matrices. This leads to characterization of idempotency of a class of triangular Toeplitz matrices. As an application, the existence of group inverse of a block triangular fuzzy matrix is discussed. Equivalent conditions for a regular block triangular fuzzy matrix to be expressed as a sum of regular block fuzzy matrices is derived. Further, fuzzy relational equations consistency is studied.     

Fuzzy关系方程保守路径的直接算法

文［１］基于布尔矩阵的保守路径给出Ｆｕｚｚｙ关系方程极小解的准确解法，但该文关于特征矩阵的定义不确切，这导致有例外的例子出现。本文给出了特征矩阵的正确定义，并设计了计算布尔矩阵保守路径个数的直接解法，使得文［１］中的方法完善化。     

On the enumeration of distinct fuzzy switching functions

This paper entails the development of bound equations on the number of minimized fuzzy switching functions. A triangular matrix representing the transitivity between fundamental fuzzy phrases is discussed and applied in the enumeration process. Improved upper and lower bounds have been developed by use of the transitivity matrix. In addition, an algorithm is described to calculate the number of distinct fuzzy switching functions for any number of variables.     

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.     

Solution to fuzzy system of linear equations with crisp coefficients

This paper presents a new and simple method to solve fuzzy real system of linear equations by solving two n × n crisp systems of linear equations. In an original system, the coefficient matrix is considered as real crisp, whereas an unknown variable vector and right hand side vector are considered as fuzzy. The general system is initially solved by adding and subtracting the left and right bounds of the vectors respectively. Then obtained solutions are used to get a final solution of the original system. The proposed method is used to solve five example problems. The results obtained are also compared with the known solutions and found to be in good agreement with them.