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What is Defuzzification

Encyclopedia of Information Science and Technology, Fifth Edition
The fuzzy logic’s approach for a problem’s solution involves the fuzzification of the problem’s data by representing them with properly defined fuzzy sets, the evaluation of the fuzzy data in order to express the problem’s solution in the form of a unique fuzzy set and the defuzzification of this fuzzy set in order to translate the problem’s mathematical solution to the natural language for use with the original real-life problem. The most popular defuzzification method is the Centre of Gravity (COG) Technique in which the corresponding system’s fuzzy outputs are represented by the coordinates of the COG of the level’s section contained between the graph of the membership function expressing those outputs and the OX axis.
Published in Chapter:
Use of Grey Numbers for Evaluating a System's Performance Under Fuzzy Conditions
Michael Voskoglou (Graduate Technological Educational Institute of Western Greece, Greece)
Copyright: © 2021 |Pages: 17
DOI: 10.4018/978-1-7998-3479-3.ch023
Abstract
In the present research, a method using Grey Numbers as tools is developed for assessing a system's mean performance, which is useful when utilizing qualitative grades and not numerical scores for this purpose. Although this new method is proved to be equivalent with an analogous method using Triangular Fuzzy Numbers as tools developed in an earlier work, it reduces the required computational burden, since it requires the calculation of two components only (instead of three in the case of the Triangular Fuzzy Numbers) for obtaining the mean value of the Grey Numbers involved. Examples are also presented on student and athlete assessment illustrating the new method and showing that the system's quality performance, calculated by the traditional GPA index, may lead to different assessment conclusions.
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Fuzzy Control Systems: An Introduction
A process that converts fuzzy terms to conventional expressions quantified by real-valued functions.
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Fuzzy Expert System in Agriculture Domain
Defuzzification is the last step in fuzzy inference mechanism. The process of converting fuzzy values from the combined output of fuzzy rules in crisp values (numerical values). The input to the defuzzification process is an aggregate set and the output from this process is a single number.
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Evaluating IBMEC-RJ’s Intranet Usability Using Fuzzy Logic
Is the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and corresponding membership degrees.
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Triangular and Trapezoidal Fuzzy Assessment Models
The process of representing a system’s fuzzy data by a crisp number.
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Multilayer Optimization Approach for Fuzzy Systems
Process of producing a quantifiable result (crisp) in fuzzy logic. Typically, a fuzzy system will have a number of rules that transform a number of variables into a “fuzzy” result, that is, the result is described in terms of membership in fuzzy sets.
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Application of Fuzzy Numbers to Assessment Processes
The process of representing a system’s fuzzy outputs by a crisp number.
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Fuzzy Outranking Methods Including Fuzzy PROMETHEE
It is the general term for the process of the creation of a crisp value as a surrogate for an existing fuzzy value. A number of defuzzification techniques are known, including centre-of-area, centre of gravity, and mean of maximums.
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Group MCDM Based on the Fuzzy AHP Approach
The process of interpreting the membership degrees of the fuzzy sets into a specific decision or real value.
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This process is reverse of fuzzification which changes the fuzzified values back to quantified values in binary logic, after the complete processing is over.
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Intuitionistic Fuzzy Image Processing
The inverse process of fuzzification. It refers to the transformation of fuzzy sets into crisp numbers.
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Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology
In fuzzification process, the crisp quantities are converted into fuzzy quantities, however in several applications as well as most of actions or decisions implemented by human or machines are binary or crisp in nature. So it is necessary to defuzzified the fuzzy results that have generated through fuzzy analysis. The process of converting the fuzzy output to a crisp value is said to be defuzzification. The input for the defuzzification process is the aggregate set and the output is a single number.
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