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What is the importance of Defuzzification in designing fuzzy inference system? 

Fuzzy number
Fuzzy classification
Fuzzy associative matrix
Fuzzy set operations
Defuzzification

Answers from top 9 papers

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Papers (9)Insight
Journal Article•DOI
Statistical analysis of the main parameters: in the fuzzy inference process
Ignacio Rojas, Julio Ortega, Francisco J. Pelayo, Alberto Prieto 
01 Mar 1999-Fuzzy Sets and Systems
28 Citations
The results obtained show that the defuzzifier and the T-norm operator are the most relevant factors in the fuzzy inference process.
Proceedings Article•DOI
Solving Fuzzy Nonlinear Systems -- A Class of Defuzzification in the Fuzzy Control
Meizhen Jia, Xiaohua Peng 
07 Dec 2013
1 Citations
It is easier to accomplish defuzzification by solving the two fuzzy nonlinear systems.
Open access•Journal Article•DOI
A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm
Antonio A. Márquez, Francisco Alfredo Márquez, Antonio Peregrín 
23 Apr 2012-International Journal of Computational Intelligence Systems
13 Citations
This paper proposes a mechanism that helps improve the interpretability of linguistic fuzzy ruled based systems with common adaptive defuzzification methods.
Journal Article•DOI
Approximation accuracy analysis of fuzzy systems as function approximators
Xiao-Jun Zeng, Madan G. Singh 
01 Feb 1996-IEEE Transactions on Fuzzy Systems
209 Citations
It is seen that the class of fuzzy systems generated by the product inference and the center-average defuzzifier has better approximation accuracy and properties than the class of fuzzy systems generated by the min inference and the center-average defuzzifier, and the class of fuzzy systems defuzzified by the MoM defuzzifier.
Proceedings Article•DOI
Fuzzy models induced by alternative defuzzification methods
Dimitar Petrov Filev, Ronald R. Yager 
08 Sep 1996
6 Citations
As a result we point out how the concept of defuzzification can applied for deriving alternative modifications of the known fuzzy models.
Journal Article•DOI
Defuzzification: criteria and classification
Werner Van Leekwijck, Etienne Kerre 
01 Dec 1999-Fuzzy Sets and Systems
701 Citations
We show that the maxima methods behave well with respect to the more basic defuzzification criteria, and hence are good candidates for fuzzy reasoning systems.
Open access•Journal Article•DOI
Similarity in fuzzy systems
Asim Pal, Banibrata Mondal, Namrata Bhattacharyya, Swapan Raha 
21 Aug 2014
11 Citations
A specificity-based approach to defuzzification is also presented, which is found to be suitable for similarity-based fuzzy systems.
Journal Article•DOI
Defuzzification techniques for fuzzy controllers
Jean J. Saade, Hassan Diab 
01 Feb 2000
55 Citations
Based on the features and disadvantages of the commonly used defuzzification techniques and on the elements involved in the structure of a fuzzy controller, a new and advantageous defuzzification technique is introduced and justified.
Journal Article•DOI
Nonlinear modelling and classification based on the MICOG defuzzification
Robert K. Nowicki
15 Dec 2009-Nonlinear Analysis-theory Methods & Applications
20 Citations
It is proven, that the MICOG defuzzification can be used in a combination with the Mamdani-type reasoning and selected S and Q fuzzy implications.

Related Questions

What are fuzzy rules?4 answersFuzzy rules are a modeling technique used to handle uncertainty and imprecision in various fields of science and engineering. They are a part of fuzzy logic, which involves fuzzy set theory and fuzzy rule systems. Fuzzy rule interpolation (FRI) is an approach that uses fuzzy rules to provide approximate decisions when a new observation does not match any existing rules. Fuzzy association rules are models that can be easily understood and interpreted. Meta-Fuzzy Items is a proposal that allows for the representation of the same information with fewer and simpler rules, improving the interpretability of fuzzy association rules. In the field of cybersecurity, YARA rules utilize fuzzy hashing and fuzzy rules to increase the effectiveness of malware detection without increasing complexity. Fuzzy rules can handle imprecise and incomplete data, making them advantageous in optimizing the performance of YARA rules during the execution phase.
What is Fuzzy logic ?5 answersFuzzy logic is a mathematical framework for reasoning about ambiguous or inaccurate information. It is founded on the idea that truth can be stated as a degree of membership in a fuzzy set rather than as a binary value of true or untrue. Fuzzy logic is used in control systems, artificial intelligence, and decision-making. It allows for arguing with boolean predicates based on confidence values between 0 and 1, and can be interpreted as probabilities. Markov kernels, parametrized probability distributions, are used to compute general fuzzy logic connectives. Fuzzy logic also allows for defining fuzzy quantifiers and estimating confidence in multivariable logic formulas. The benefits of fuzzy logic include handling uncertainty and ambiguity, combining human knowledge with computing, and improving decision-making in medical diagnosis. It can also be applied to control energy sources and reduce energy consumption in daily life.
What are the fundamental concepts of fuzzy calculus?5 answersThe fundamental concepts of fuzzy calculus include fractal and fuzzy calculus, which involve fractal limits, continuity, derivatives, and integrals. Fractal fuzzy calculus is a new framework that incorporates fractal fuzzy derivatives and integrals, with fuzzy number-valued functions as solutions to fractal fuzzy differential equations. Additionally, there are introduced calculi such as $^\oplus$calculus and $^\otimes$calculus for intuitionistic fuzzy values, which have isomorphic mappings to interpret their relationships. These calculi also extend to fuzzy sets, providing new calculi for them. Furthermore, fuzzy calculus encompasses differential and integral branches, with rules for finding limits, derivatives, and integrals of fuzzy-valued functions, as well as theorems such as the fuzzy intermediate value theorem and mean value theorem.
What are the different definitions of fuzzy derivatives?5 answersDifferent definitions of fuzzy derivatives have been proposed in the literature. One definition is the Modified Hukuhara derivative, which is used for fuzzy differentiation of fuzzy valued functions. Another definition is based on Caratheodory's derivative notion and is used to prove properties of fuzzy derivatives, such as Rolle's theorem and the Generalized Mean-Value Theorem. Additionally, there are definitions of fuzzy derivatives for type-2 fuzzy number-valued functions of fractional order, such as the Riemann-Liouville and Caputo derivatives. Furthermore, a different approach to fuzzy derivatives is introduced, where they are used as a mathematical tool to describe the relationship between parameters in systems, such as in the classification of liver disorders.
What are the main steps in fuzzy inference system?9 answers
Which fuzzy inference system is used more and why?10 answers

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