Membership function estimation is one of the less explored, albeit important, areas in fuzzy sets. This paper aims to define a new family of fuzzy sets called general continuous linguistic variables (GCLV), which represents a linguistic variable rather than a set of linguistic values. We refer to it as the principle of representation of linguistic variables. They are based on the well-known sigmoidal functions and contain at least three different classes of membership functions, namely, an increasing sigmoidal function, a decreasing sigmoidal function, and a convex one. These diverse features are essential to represent linguistic values exhibiting different semantics. We explore the properties of GCLV, including those ones over that allow us to approximate every continuous membership function. Finally, we illustrate the applicability of GCLV as a fuzzy tool. This leads to the development of the foundations of a new vehicle in fuzzy sets useful in data mining and time series prediction.

隶属度函数估计是模糊集中较少探索但重要的领域之一。本文旨在定义一个称为通用连续语言变量（GCLV）的模糊集新系列，该系列代表一种语言变量而不是一组语言值。我们称其为语言变量的表示原理。它们基于众所周知的S形函数，并且包含至少三种不同的隶属函数类，即递增的S形函数，递减的S形函数和凸形。这些不同的功能对于表示表现出不同语义的语言价值至关重要。我们探索了GCLV的属性，包括那些使我们能够近似每个连续隶属函数的属性。最后，我们说明了GCLV作为模糊工具的适用性。这导致了新车的模糊集基础的发展，这些集对数据挖掘和时间序列预测很有用。