Information boundedness principle in fuzzy inference process

Peter Sarkoci; Michal Šabo

Kybernetika (2002)

  • Volume: 38, Issue: 3, page [327]-338
  • ISSN: 0023-5954

Abstract

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The information boundedness principle requires that the knowledge obtained as a result of an inference process should not have more information than that contained in the consequent of the rule. From this point of view relevancy transformation operators as a generalization of implications are investigated.

How to cite

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Sarkoci, Peter, and Šabo, Michal. "Information boundedness principle in fuzzy inference process." Kybernetika 38.3 (2002): [327]-338. <http://eudml.org/doc/33586>.

@article{Sarkoci2002,
abstract = {The information boundedness principle requires that the knowledge obtained as a result of an inference process should not have more information than that contained in the consequent of the rule. From this point of view relevancy transformation operators as a generalization of implications are investigated.},
author = {Sarkoci, Peter, Šabo, Michal},
journal = {Kybernetika},
keywords = {inference; fuzzy system modeling; inference; fuzzy system modeling},
language = {eng},
number = {3},
pages = {[327]-338},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Information boundedness principle in fuzzy inference process},
url = {http://eudml.org/doc/33586},
volume = {38},
year = {2002},
}

TY - JOUR
AU - Sarkoci, Peter
AU - Šabo, Michal
TI - Information boundedness principle in fuzzy inference process
JO - Kybernetika
PY - 2002
PB - Institute of Information Theory and Automation AS CR
VL - 38
IS - 3
SP - [327]
EP - 338
AB - The information boundedness principle requires that the knowledge obtained as a result of an inference process should not have more information than that contained in the consequent of the rule. From this point of view relevancy transformation operators as a generalization of implications are investigated.
LA - eng
KW - inference; fuzzy system modeling; inference; fuzzy system modeling
UR - http://eudml.org/doc/33586
ER -

References

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  1. Dubois D., Prade H., Properties of measures of information in evidence and possibility theories, Fuzzy Sets and Systems 100 (1999), Supplement, 35–49 (1999) MR1177947
  2. Klir G. J., Bo Y., Fuzzy Sets and Fuzzy Logic, Theory and Applications. Prentice–Hall, Englewood Cliffs, N. J. 1995 Zbl0915.03001MR1329731
  3. Kolesárová A., Kerre E. E., Computational rule of inference based on triangular norms, In: Fuzzy If–Then Rules in Computational Inteligence. Theory and Applications (Da Ruan and E. E. Kerre, eds.), Kluwer Academic Publishers, Dordrecht 2000, pp. 61–80 
  4. Nelsen R. B., An Introduction to Copulas, Lecture Notes in Statistics, Springer, Berlin 1999 Zbl1152.62030MR1653203
  5. Šabo M., Kolesárová, A., Varga Š., 10.1142/S0218488501000715, Internat. J. Uncertainty and Knowledge–Based Systems 9 (2001), 2, 169–181 Zbl1113.68504MR1821986DOI10.1142/S0218488501000715
  6. Yager R. R., Global requirements for implication operators in fuzzy modus ponens, Fuzzy Sets and Systems 106 (1999), 3–10 (1999) Zbl0931.68117MR1689566
  7. Yager R. R., 10.1016/S0165-0114(00)00027-0, Fuzzy Sets and Systems 122 (2001), 167–175 MR1839955DOI10.1016/S0165-0114(00)00027-0

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