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引用次数: 0
摘要
在本文中,我们研究了基于最大积构成或最大卢卡谢维奇构成的模糊关系方程系统的不一致性。给定一个不一致系统,我们通过一个明确的分析公式计算出不一致系统的右侧向量与用相同矩阵定义的一致系统的右侧向量集(即不一致系统的右侧向量)之间的切比雪夫距离(由 L-infinity 准则定义)。使我们能够获得这些公式的主要结果是,切比雪夫距离是满足向量不等式的最小正值,无论在组合中使用的是什么 t 准则。然后,我们给出了计算不一致系统右边向量的最大切比雪夫近似值及其最大近似解的明确分析公式。通过与 max-Lukasiewicz 系统相关的切比雪夫距离分析公式,我们可以证明对于 max-Lukasiewicz 组合来说可逆的模糊矩阵是置换矩阵。
On the handling of inconsistent systems based on max-product and max-Lukasiewicz compositions
In this article, we study the inconsistency of systems of fuzzy relational equations based either on the max-product composition or on the max-Lukasiewicz composition. Given an inconsistent system, we compute by an explicit analytical formula the Chebyshev distance (defined by L-infinity norm) between the right-hand side vector of the inconsistent system and the set of right-hand side vectors of consistent systems defined with the same matrix: that of the inconsistent system. The main result that allows us to obtain these formulas is that the Chebyshev distance is the smallest positive value that satisfies a vector inequality, whatever the t-norm used in the composition. We then give explicit analytical formulas for computing the greatest Chebyshev approximation of the right-hand side vector of an inconsistent system and its greatest approximate solution. The analytical formula of the Chebyshev distance associated with max-Lukasiewicz systems allows us to show that the fuzzy matrices which are invertible for max-Lukasiewicz composition are the permutation matrices.
期刊介绍:
Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies.
In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.