Min-max inference for Possibilistic Rule-Based System
Résumé
In this paper, we explore the min-max inference mechanism of any rule-based system of n if-then possibilistic rules. We establish an additive formula for the output possibility distribution obtained by the inference. From this result, we deduce the corresponding possibility and necessity measures. Moreover, we give necessary and sufficient conditions for the normalization of the output possibility distribution. As application of our results, we tackle the case of a cascade of two if-then possibilistic rules sets and establish an input-output relation between the two min-max equation systems. Finally, we associate to the cascade construction an explicit min-max neural network.
Mots clés
necessity measures
inference mechanisms
fuzzy set theory
fuzzy neural nets
Task analysis
Neural network
Inference mechanisms
Sensitivity analysis
Additives
Sufficient conditions
online learning
machine learning
fuzzy logic
artificial intelligence
knowledge based systems
linear systems
minimax techniques
piecewise linear techniques
possibility theory
rule-based system
possibilistic rule-based system
min-max inference mechanism
additive formula
output possibility distribution
corresponding possibility
Domaines
Intelligence artificielle [cs.AI]
Fichier principal
article_IsmailBaaj_FUZZ_IEEE_2021_min_max_inference_for_possibilistic_rule_based_system.pdf (323.93 Ko)
Télécharger le fichier
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|