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Bibliographische Detailangaben
Hauptverfasser: La Bella, Alessio, Farina, Marcello, D'Amico, William, Zaccarian, Luca
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2409.15792
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Inhaltsangabe:
  • In this paper we propose novel global and regional stability analysis conditions based on linear matrix inequalities for a general class of recurrent neural networks. These conditions can be also used for state-feedback control design and a suitable optimization problem enforcing H2 norm minimization properties is defined. The theoretical results are corroborated by numerical simulations, showing the advantages and limitations of the methods presented herein.