Linguistic rules in natural language are useful and consistent with human way of thinking. They are very important in multi-criteria decision making due to their interpretability. In this paper, our discussions concentrate on extracting linguistic rules from data sets. In the end, we firstly analyze how to extract complex linguistic data summaries based on fuzzy logic. Then, we formalize lingui...

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values and set the minimum supports and minimum confidences at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural ...

Nowadays, an abundant amount of information is created and delivered over electronic media. The information gathering in Internet is a complex activity and Internet users need tools to assist them to find the information required. Web multi-agent systems assist the users by gathering from Internet the information that best satisfies their specific needs. In this paper, we analyze some technique...

Abstract The purpose of this paper is to extend the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for solving multiple attribute decision making (MADM) problems with trapezoid fuzzy linguistic variables. To begin with, this paper introduces the concept of the trapezoid fuzzy linguistic variables, and defines the distance between two trapezoid fuzzy linguistic v...

Fuzzy logic control is a methodology bridging artificial intelligence and traditional control theory. This methodology is usually applied in the only cases when accuracy is not of high necessity or importance. On the other hand, as it is stated in (TI SPRA028, Jan.1993), “Fuzzy Logic can address complex control problems, such as robotic arm movement, chemical or manufacturing process control, a...

since the pioneering work of zadeh, fuzzy set theory has been applied to amyriad of areas. song and chissom introduced the concept of fuzzy time series andapplied some methods to the enrolments of the university of alabama. thereafter weapply fuzzy techniques for system identification and apply statistical techniques tomodelling system. an automatic methodology framework that combines fuzzytech...

The generalization of two-valued (crisp) sets to fuzzy sets gave us a rich and powerful representation scheme within the domain of semantic uncertainty. A parallel generalization of conjunctions and disjunction is needed from tnorms and t-conorms to what we might appropriately call linguistic (fuzzy) connectives, “AND," “OR," etc. In fact a closer look at the canonical forms of the combination ...

We propose a method for incorporating multiple linguistic variables into a system dynamics framework. A simple example is used to illustrate the procedures necessary to define linguistic variables using triangular membership functions within the VENSIM Simulation Environment. We illustrate the operations of linguistic variables through a sales and service model where two linguistic variables i....

Multimodel approaches are widely used with linear submodels, but border areas around submodels are problematic. Special cases of fuzzy linguistic equation models, which can be understood as linguistic Takagi-Sugeno (LTS) type fuzzy models, can be used to solve these problems in many cases. These models use a special nonlinear scaling approach for both inputs and outputs. The LTS models are robu...