Complex fuzzy sets (CFSs) are a suitable tool to manage spatial directional information which includes distance and direction. However, is given by linguistic values. It very awkward for the CFS describe this type of information. To overcome limitation, we first propose novel concept called complex set (LCFS) serve as an extension CFS. Then put forward some basic operational laws LCFSs. After t...

A new systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented in this paper. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism...

The intuitionistic uncertain fuzzy linguistic variable can easily express the fuzzy information, and the power average (PA) operator is a useful tool which provides more versatility in the information aggregation procedure. At the same time, Einstein operations are a kind of various t-norms and t-conorms families which can be used to perform the corresponding intersections and unions of intuiti...

Information Retrieval Systems (IRSs) based on an ordinal fuzzy linguistic approach present some problems of loss of precision and information when working with discrete linguistic expression domains or when applying approximation operations in the symbolic aggregation methods. In this paper, we present an IRS based on a 2tuple fuzzy linguistic approach which allows us to overcome the problems o...

The fuzzy sets defining the linguistic variable values can be seen as a fuzzy partition of the linguistic variable. The membership functions obtained using fuzzy clustering algorithms are defined with respect to the group prototypes, and they cannot be used to define the linguistic variable values. We introduce several criteria to pass from the clustering membership functions to the linguistic ...

Information Retrieval Systems (IRSs) based on an ordinal fuzzy linguistic approach present some problems of loss of information and lack of precision when working with discrete linguistic expression domains or when applying approximation operations in the symbolic aggregation methods. In this paper, we present a new IRS model based on the 2-tuple fuzzy linguistic approach, which allows us to ov...

Assuming a group decision making problem where the experts express their opinions by means of linguistic preference relations, the application of the Linguistic OWA operator guided by fuzzy majority is analyzed. Two diierent perspectives of the use of the LOWA operator are presented: (i) in the selection process, to aggregate individual linguistic preference relations in a collective one and to...

This chapter discusses several issues related to the design of linguistic models with high interpretability using fuzzy genetics-based machine learning (GBML) algorithms. We assume that a set of linguistic terms has been given for each variable. Thus our modelling task is to find a small number of fuzzy rules from possible combinations of the given linguistic terms. First we formulate a threeob...

Fuzzy set theory provides a framework in which natural language expressions can be modelled mathematically, thereby even taking the vagueness of linguistic terms into account. In fuzzy systems a linguistic term (e.g. fast) is represented by a fuzzy set, while a linguistic modifier (e.g. very) is modelled by an operator (also known as hedge) transforming a fuzzy set into another. In the paper fi...

Hesitant fuzzy linguistic term sets were introduced to grasp the uncertainty existing in human reasoning. In this paper, inspired by absolute order-of-magnitude qualitative reasoning techniques, an extension of the set of hesitant fuzzy linguistic term sets is presented to capture differences between non-compatible preferences. In addition, an order relation and two closed operations over this ...