Methodological issues of the fuzzy set theory (generalizing article)

A. I. Orlov
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Abstract

The theory of fuzziness is an important area of modern theoretical and applied mathematics. The methodology of the theory of fuzziness is a doctrine of organizing activities in the field of development and application of the scientific results of this theory. We discuss some methodological issues of the theory of fuzziness, i.e., individual components of the methodology in the area under consideration. The theory of fuzziness is a science of pragmatic (fuzzy) numbers and sets. The ancient Greek philosopher Eubulides showed that the concepts «Heap» and «Bald» cannot be described using natural numbers. E. Borel proposed to define a fuzzy set using a membership function. A fundamentally important step was taken by L. A. Zadeh in 1965. He gave the basic definitions of the algebra of fuzzy sets and introduced the operations of intersection, product, union, sum, negation of fuzzy sets. The main thing he did was demonstration of the possibilities of expanding («doubling») mathematics: by replacing the numbers and sets used in mathematics with their fuzzy counterparts, we obtain new mathematical formulations. In the statistics of non-numerical data, methods of statistical analysis of fuzzy sets have been developed. Interval and triangular fuzzy numbers are often used specific types of membership functions. The theory of fuzzy sets in a certain sense is reduced to the theory of random sets. We think fuzzy and that is the only reason we understand each other. The paradox of the fuzzy theory is that it is impossible to consistently implement the thesis «Everything in the world is fuzzy». For ordinary fuzzy sets, the argument and values of the membership function are crisp. If they are replaced by fuzzy analogues, then their description will require their own clear arguments and membership functions, and so on ad infinitum. System fuzzy interval mathematics proceeds from the need to take into account the fuzziness of the initial data and the prerequisites of the mathematical model. One of the options for its practical implementation is an automated system-cognitive analysis and the intellectual system «Eidos».
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模糊集合论的方法论问题(概括性文章)
模糊理论是现代理论数学和应用数学的一个重要领域。模糊理论的方法论是在开发和应用该理论的科学成果领域组织活动的理论。我们将讨论模糊性理论的一些方法论问题,即该领域方法论的各个组成部分。模糊理论是一门关于实用(模糊)数和集的科学。古希腊哲学家欧布里得斯(Eubulides)指出,"堆 "和 "秃头 "这两个概念无法用自然数来描述。E. Borel 提出用成员函数定义模糊集。1965 年,L. A. Zadeh 迈出了重要的一步。他给出了模糊集代数的基本定义,并引入了模糊集的交、积、并、和、否定等运算。他所做的主要工作是展示数学扩展("加倍")的可能性:通过用模糊对应物替换数学中使用的数和集合,我们可以得到新的数学公式。在非数字数据的统计中,模糊集的统计分析方法得到了发展。区间模糊数和三角模糊数是经常使用的特定类型的成员函数。模糊集理论在一定意义上被简化为随机集理论。我们认为模糊,这是我们相互理解的唯一原因。模糊理论的悖论在于,它不可能始终如一地实现 "世界万物都是模糊的 "这一论断。对于普通模糊集来说,成员函数的参数和值是清晰的。如果用模糊类似集来代替它们,那么对它们的描述就需要有自己明确的参数和成员函数,如此循环往复,无穷无尽。系统模糊区间数学从需要考虑初始数据的模糊性和数学模型的先决条件出发。自动系统认知分析和智能系统 "Eidos "是实际应用的选择之一。
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