Publicación: Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks
Fecha
2021
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info:eu-repo/semantics/openAccess
Título de la revista
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IEEE
Resumen
Several designs of recurrent neural networks have been proposed in the literature involving different clock times. However, the stability and synchronization of this kind of system have not been studied. In this paper, we consider that each neuron or group of neurons of a switched recurrent neural network can have a different sampling period for its activation, which we call switched multi-rate recurrent neural networks, and we propose a dynamical model to describe it. Through Lyapunov methods, sufficient conditions are provided to guarantee the exponential stability of the network. Additionally, these results are extended to the synchronization problem of two identical networks, understanding the synchronization as the agreement of both of them in time. Numerical simulations are presented to validate the theoretical results. The proposed method might help to design more efficient and less computationally demanding neural networks.
Descripción
The registered version of this article, first published in “IEEE Access, vol. 9", is available online at the publisher's website: IEEE, https://doi.org/10.1109/ACCESS.2021.3067452
La versión registrada de este artículo, publicado por primera vez en “IEEE Access, vol. 9", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/ACCESS.2021.3067452
Categorías UNESCO
Palabras clave
recurrent neural networks, multi-rate systems, switched systems, Lyapunov methods
Citación
V. Ruiz, E. Aranda-Escolástico, J. Salt and S. Dormido, "Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks," in IEEE Access, vol. 9, pp. 45614-45621, 2021, doi: 10.1109/ACCESS.2021.3067452.
Centro
Facultades y escuelas
Departamento
Ingeniería de Software y Sistemas Informáticos