scholarly journals Introduction to fuzzy logic

2005 ◽  
Vol 18 (2) ◽  
pp. 319-328 ◽  
Author(s):  
Claudio Moraga
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Main Emphasis ◽  
New Applications

This paper gives basics and reviews some classical as well as new applications of fuzzy logic. The main emphasis of the paper is on fuzzy decision making under a linguistic view of fuzzy sets.

scholarly journals Fuzzy sets and fuzzy decision making in nutrition

1997 ◽  
Vol 51 (5) ◽  
pp. 286-296 ◽  
Author(s):  
B Wirsam ◽  
A Hahn ◽  
EO Uthus ◽  
C Leitzmann
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision

scholarly journals Dual Extended Hesitant Fuzzy Sets

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 714 ◽  
Author(s):  
José Carlos R. Alcantud ◽  
Gustavo Santos-García ◽  
Xindong Peng ◽  
Jianming Zhan
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Natural Extension ◽  
Score Function ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Hesitant Fuzzy Sets ◽  
Weight Score ◽  
Decision Making Problem

Hesitant fuzzy sets extend fuzzy sets by considering many-valued sets of membership degrees. Real applications validate this model and decision making approaches of various forms permit to act in a flexible manner. If we can avail ourselves of hesitant information on non-membership degrees too, then dual hesitant fuzzy sets provide a natural extension of both hesitant fuzzy sets and intuitionistic fuzzy sets. This article defines the concept of dual extended hesitant fuzzy set as the combination of extended hesitant fuzzy sets with dual hesitant fuzzy sets. Its basic algebraic properties are set forth, and the model is linked to other successful models in the literature. We also define a comparison law for the prioritization of elements described in this new framework. Moreover, we present an algorithm to solve the dual extended hesitant fuzzy decision making problem by a weight score function. Finally, the feasibility of this approach is demonstrated by the evaluation of big data industries with an effectiveness test.

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

scholarly journals New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets

Axioms ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 73 ◽  
Author(s):  
Saida Mohamed ◽  
Areeg Abdalla ◽  
Robert John
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Intuitionistic Fuzzy Sets ◽  
Entropy Measure ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
New Approach ◽  
Interval Valued

In this paper, we propose a new approach to constructing similarity measures using the entropy measure for Interval-Valued Intuitionistic Fuzzy Sets. In addition, we provide several illustrative examples to demonstrate the practicality and effectiveness of the proposed formula. Finally, we use the new proposed similarity measure to develop a new approach for solving problems of pattern recognition and multi-criteria fuzzy decision-making.

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