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Completeness and consistency conditions for learning fuzzy rules

Authors:

Antonio Gonzalez,

Raul PerezAuthors Info & Claims

Fuzzy Sets and Systems, Volume 96, Issue 1

Pages 37 - 51

https://doi.org/10.1016/S0165-0114(96)00280-1

Published: 16 May 1998 Publication History

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Garcia DGamez JGonzalez APerez R(2021)Using a sequential covering strategy for discovering fuzzy rules incrementally2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2015.7337924(1-8)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/FUZZ-IEEE.2015.7337924
Liu RWang FHe MJiao L(2019)An adjustable fuzzy classification algorithm using an improved multi-objective genetic strategy based on decomposition for imbalance datasetKnowledge and Information Systems10.1007/s10115-019-01342-561:3(1583-1605)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10115-019-01342-5
Kerr-Wilson JPedrycz W(2017)Some new qualitative insights into quality of fuzzy rule-based modelsFuzzy Sets and Systems10.1016/j.fss.2016.05.002307:C(29-49)Online publication date: 15-Jan-2017
https://dl.acm.org/doi/10.1016/j.fss.2016.05.002
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Completeness and consistency conditions for learning fuzzy rules

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Published In

Fuzzy Sets and Systems Volume 96, Issue 1

May 16, 1998

118 pages

ISSN:0165-0114

Editor:
H.-J Zimmermann
RWTH Aachen, Aachen, Germany

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Elsevier North-Holland, Inc.

United States

Publication History

Published: 16 May 1998

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Garcia DGamez JGonzalez APerez R(2021)Using a sequential covering strategy for discovering fuzzy rules incrementally2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2015.7337924(1-8)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/FUZZ-IEEE.2015.7337924
Liu RWang FHe MJiao L(2019)An adjustable fuzzy classification algorithm using an improved multi-objective genetic strategy based on decomposition for imbalance datasetKnowledge and Information Systems10.1007/s10115-019-01342-561:3(1583-1605)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10115-019-01342-5
Kerr-Wilson JPedrycz W(2017)Some new qualitative insights into quality of fuzzy rule-based modelsFuzzy Sets and Systems10.1016/j.fss.2016.05.002307:C(29-49)Online publication date: 15-Jan-2017
https://dl.acm.org/doi/10.1016/j.fss.2016.05.002
(2016)An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIsKnowledge-Based Systems10.1016/j.knosys.2016.01.02697:C(1-10)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.01.026
Gámez JGarcía DGonzález APérez R(2016)Ordinal classification based on the sequential covering strategyInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.05.00276:C(96-110)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.ijar.2016.05.002
García DGámez JGonzález APérez R(2015)An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approachInternational Journal of Approximate Reasoning10.1016/j.ijar.2015.09.00167:C(37-58)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1016/j.ijar.2015.09.001
Antonelli MDucange PMarcelloni F(2014)An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasetsNeurocomputing10.1016/j.neucom.2014.04.070146:C(125-136)Online publication date: 15-Oct-2014
https://dl.acm.org/doi/10.1016/j.neucom.2014.04.070
Mozaffari ABehzadipour SKohani M(2014)Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisorApplied Soft Computing10.1016/j.asoc.2013.09.02314(12-30)Online publication date: 1-Jan-2014
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Kumar MStoll NStoll R(2010)Variational bayes for a mixed stochastic/deterministic fuzzy filterIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2010.204833118:4(787-801)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.1109/TFUZZ.2010.2048331
Sánchez LCouso ICasillas J(2009)Genetic learning of fuzzy rules based on low quality dataFuzzy Sets and Systems10.1016/j.fss.2009.03.004160:17(2524-2552)Online publication date: 1-Sep-2009
https://dl.acm.org/doi/10.1016/j.fss.2009.03.004
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Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms

On the representation of fuzzy rules in terms of crisp rules

Fuzzy Interpolative Reasoning for Sparse Fuzzy-Rule-Based Systems Based on the Areas of Fuzzy Sets

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