Algorithmes évolutionnaires multi-objectifs pour les réseaux neuronaux de pointe
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Algorithmes évolutionnaires multi-objectifs pour les réseaux neuronaux de pointe

Algorithmes évolutionnaires multi-objectifs pour les réseaux neuronaux de pointe


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About the Book

Les réseaux de neurones à pointes (SNN) jouent un rôle essentiel dans les problèmes de classification. Bien qu'il existe de nombreux modèles de SNN, l'Evolving Spiking Neural Network (ESNN) est largement utilisé dans de nombreux travaux de recherche récents. Les algorithmes évolutionnaires, principalement l'évolution différentielle (DE), ont été utilisés pour améliorer l'algorithme ESNN. Cependant, de nombreux problèmes d'optimisation dans le monde réel comportent plusieurs objectifs contradictoires. Plutôt qu'une optimisation unique, l'optimisation multi-objectifs (MOO) peut être utilisée comme un ensemble de solutions optimales pour résoudre ces problèmes. Dans cet ouvrage, la recherche harmonique (HS) et l'approche mémétique ont été utilisées pour améliorer les performances de la MOO avec le réseau neuronal à pointes évolutives (ESNN). Par conséquent, l'évolution différentielle multiobjective à recherche d'harmonie mémétique avec réseau neuronal à pointes évolutif (MEHSMODE-ESNN) a été appliquée pour améliorer la structure et les taux de précision de l'ESNN. Des ensembles de données standard provenant de l'apprentissage automatique de l'UCI sont utilisés pour évaluer les performances de ce modèle hybride multiobjectif amélioré.


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Product Details
  • ISBN-13: 9786208557492
  • Publisher: Editions Notre Savoir
  • Publisher Imprint: Editions Notre Savoir
  • Height: 229 mm
  • No of Pages: 60
  • Returnable: N
  • Weight: 145 gr
  • ISBN-10: 6208557496
  • Publisher Date: 01 Feb 2025
  • Binding: Paperback
  • Language: French
  • Returnable: N
  • Spine Width: 4 mm
  • Width: 152 mm


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