Optimizing parameter settings for hopfield neural networks using reinforcement learning

Hopfield neural network stands out from artificial neural network models for solving optimization problems due to its unique ability to rapidly converge on global solutions without requiring complex supervised learning steps, although the performance of this network depends mainly on the tuning of i...

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Veröffentlicht in:	Evolving systems 2024-12, Vol.15 (6), p.2419-2440
Hauptverfasser:	Rbihou, Safae, Joudar, Nour-Eddine, Haddouch, Khalid
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Artificial neural networks Complex Systems Complexity Decision making Deep learning Effectiveness Engineering Expected values Neural networks Optimization Optimization techniques Original Paper Supervised learning Tuning
Online-Zugang:	Volltext
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