research-article

Selection of appropriate defuzzification methods using application specific properties

Author:

T. A. RunklerAuthors Info & Claims

IEEE Transactions on Fuzzy Systems, Volume 5, Issue 1

Pages 72 - 79

https://doi.org/10.1109/91.554449

Published: 01 February 1997 Publication History

Abstract

Defuzzification is used to transform fuzzy inference results into crisp output. The standard defuzzification methods fail in some applications. It is, therefore, important to select appropriate defuzzification methods depending on the application. This paper presents some of the most important defuzzification methods and investigates their properties. With three application examples, it illustrates how to select appropriate defuzzification methods using application specific properties

Cited By

View all

Luukka PStoklasa J(2024)Possibilistic mean based defuzzification for fuzzy expert systems and fuzzy control—LSD for general fuzzy setsJournal of Computational and Applied Mathematics10.1016/j.cam.2023.115663441:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.cam.2023.115663
Pourabdollah AAcampora GSchiattarella R(2022)Implementing Defuzzification Operators on Quantum Annealers2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882576(1-6)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/FUZZ-IEEE55066.2022.9882576
Jahanandish RKhosravifard AVatankhah RKahraman C(2020)Determination of uncertain parameters of a two-axis gimbal and motion tracking via Fuzzy logic control approachJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18911939:5(6565-6577)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-189119
Show More Cited By

Index Terms

Selection of appropriate defuzzification methods using application specific properties
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic

Index terms have been assigned to the content through auto-classification.

Recommendations

Properties of interval type-2 defuzzification operators
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Interval type-2 defuzzification maps an interval type-2 fuzzy set to a crisp number. We show that the semantic meaning of the interval type-2 fuzzy set (the associated opportunity or risk) has to be considered in the choice of an appropriate interval type-...
Selection of appropriate defuzzification methods

We propose an approach for selecting appropriate defuzzification methods that fulfills the requirements of a group of experts.We propose a sensitivity analysis of defuzzification methods.We apply this approach to the assessment of dam ...
Intuitionistic fuzzy c-control charts using defuzzification and likelihood methods

Control chart is one of the statistical methods to analyze the process. The use of fuzzy sets in control charts, which are divided into qualitative and quantitative data, has been applied in many studies recently. Especially for qualitative control charts,...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Fuzzy Systems

IEEE Transactions on Fuzzy Systems Volume 5, Issue 1

February 1997

155 pages

ISSN:1063-6706

Issue’s Table of Contents

Publisher

IEEE Press

Publication History

Published: 01 February 1997

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Luukka PStoklasa J(2024)Possibilistic mean based defuzzification for fuzzy expert systems and fuzzy control—LSD for general fuzzy setsJournal of Computational and Applied Mathematics10.1016/j.cam.2023.115663441:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.cam.2023.115663
Pourabdollah AAcampora GSchiattarella R(2022)Implementing Defuzzification Operators on Quantum Annealers2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882576(1-6)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/FUZZ-IEEE55066.2022.9882576
Jahanandish RKhosravifard AVatankhah RKahraman C(2020)Determination of uncertain parameters of a two-axis gimbal and motion tracking via Fuzzy logic control approachJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18911939:5(6565-6577)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-189119
Yeh CKuo Y(2020)Fuzzy Multi-Criteria Selection of Non-Ferrous Scrap Metal Suppliers2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283322(841-847)Online publication date: 11-Oct-2020
https://dl.acm.org/doi/10.1109/SMC42975.2020.9283322
S.Sankar P.Srinivasan (2019)Fuzzy Sets Based Cluster Routing Protocol For Internet of ThingsInternational Journal of Fuzzy System Applications10.4018/IJFSA.20190701038:3(70-93)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.4018/IJFSA.2019070103
Toledo-Castro JCaballero-Gil PRodríguez-Pérez NSantos-González IHernández-Goya CAguasca-Colomo RSchumann A(2018)Forest Fire Prevention, Detection, and Fighting Based on Fuzzy Logic and Wireless Sensor NetworksComplexity10.1155/2018/16397152018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/1639715
Kahtan HZamli KFatthi WAbdullah AAbdulleteef MKamarulzaman N(2018)Heart Disease Diagnosis System Using Fuzzy LogicProceedings of the 2018 7th International Conference on Software and Computer Applications10.1145/3185089.3185118(297-301)Online publication date: 8-Feb-2018
https://dl.acm.org/doi/10.1145/3185089.3185118
Talon ACurt C(2017)Selection of appropriate defuzzification methodsExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.09.00470:C(160-174)Online publication date: 15-Mar-2017
https://dl.acm.org/doi/10.1016/j.eswa.2016.09.004
S. KT. RK. M(2017)Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft setsApplied Soft Computing10.1016/j.asoc.2016.10.01552:C(532-548)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.asoc.2016.10.015
Das Adhikary DMallick D(2017)A Congestion Aware, Energy Efficient, on Demand Fuzzy Logic Based Clustering Protocol for Multi-hop Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-017-4581-997:1(1445-1474)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s11277-017-4581-9
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Index Terms

Recommendations

Properties of interval type-2 defuzzification operators