Logic-oriented fuzzy neural networks: A survey
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Notice bibliographique
Résumé
• The work presents a comprehensive review and discussion of logic-oriented fuzzy AND\OR network. • The architecture exhibits outstanding learning and interpretation capabilities. • Logic-oriented neurons are endowed with clearly delineated logic structure. • Interpretability is the main advantage of the neurons that make the model linguistically readable. • Knowledge is extracted in the form that is easily comprehended by humans. Data analysis and their thorough interpretation have posed a substantial challenge in the era of big data due to increasingly complex data structures and their sheer volumes. The black-box nature of neural networks may omit important information about why certain predictions have been made which makes it difficult to ground the reliability of a prediction despite tremendous successes of machine learning models. Therefore, the need for reliable decision-making processes stresses the significance of interpretable models that eliminate uncertainty, supporting explainability while maintaining high generalization capabilities. Logic-oriented fuzzy neural networks are capable to cope with a fundamental challenge of fuzzy system modeling. They strike a sound balance between accuracy and interpretability because of the underlying features of the network components and their logic-oriented characteristics. In this survey, we conduct a comprehensive review of logic-oriented fuzzy neural networks with a special attention being directed to AND\OR architecture. The architectures under review have shown promising results, as reported in the literature, especially when extracting useful knowledge through building experimentally justifiable models. Those models show balance between accuracy and interpretability because of the prefect integration between the merits of neural networks and fuzzy logic which has led to reliable decision-making processes. The survey discusses logic-oriented networks from different perspectives and mainly focuses on the augmentation of interpretation through vast array of learning abilities. This work is significantly important due to the lack to similar survey in the literature that discusses this particular architecture in depth. Finally, we stress that the architecture could offer a novel promising processing environment if they are integrated with other fuzzy tools which we have discussed thoroughly in this paper.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle