Introduction and Trends to Fuzzy Logic and Fuzzy Databases

Abstract

This chapter presents an introduction to fuzzy logic and to fuzzy databases. With regard to the first topic, we have introduced the main concepts in this field to facilitate the understanding of the rest of the chapters to novel readers in fuzzy subjects. With respect to the fuzzy databases, this chapter gives a list of six research topics in this fuzzy area. All these topics are briefly commented on, and we include references to books, papers, and even to other chapters of this handbook, where we can find some interesting reviews about different subjects and new approaches with different goals. Finally, we give a historic summary of some fuzzy models, and we conclude with some future trends in this scientific area.

Autonomous biomimetic solid dispensing using a dual-arm robotic manipulator

“…17,18 The Mamdani type inference minimum function, also known as the max–min inference method, is employed for the inference step 15 because this provides improved interpretability when using the rule base. 19…”

“…17,18 The Mamdani type inference minimum function, also known as the max-min inference method, is employed for the inference step 15 because this provides improved interpretability when using the rule base. 19 FLC1 is considered to be a system with two inputs and one output. The membership function for input 1particle size (Fig.…”

An expert system approach for the choice of appropriate data types in a Fuzzy Database

“…The FDB models are considered in a very simple shape and consist in adding a degree, normally in the interval [0, 1], to every tuple [9]. This degree allows maintaining the homogeneity of the data in the DB.…”

“…The processing of the data will be different depending on the meaning. The most important possible meanings of the degrees used by some authors are: fulfillment degree, uncertainty degree, possibility degree and importance degree [9,11]. The most typical kind of degree is a degree associated to each tuple in a relation (Type 7) with the meaning of membership degree of each tuple to the relation.…”

The Complex Evaluation of the Impact of COVID-19 Pandemic at Universities: A Soft Computing Approach

“…In this environment, questionnaires should be tailored to each group. To solve this task, we adopted concepts from fuzzy sets and fuzzy logic in general (e.g., Galindo, 2008;Kacprzyk and Zadrozny, 2009), the theory of aggregation functions (summarized in, e.g., Grabisch et al, 2009;Grabisch, 2003), and the method for flexible data collection (including hesitance) and evaluation of answers proposed in Zapletal et al (2023). This article explores the experience and satisfaction with distance learning during the COVID-19 pandemic compared to before and after the pandemic situation (or at least when restrictions were relaxed) at selected European universities.…”