lim-exponentialstability-2021.pdf (4.5 MB)
Exponential stability in the Lagrange sense for Clifford-valued recurrent neural networks with time delays
journal contribution
posted on 2021-12-01, 00:00 authored by G Rajchakit, R Sriraman, N Boonsatit, P Hammachukiattikul, Chee Peng LimChee Peng Lim, P AgarwalAbstractThis paper considers the Clifford-valued recurrent neural network (RNN) models, as an augmentation of real-valued, complex-valued, and quaternion-valued neural network models, and investigates their global exponential stability in the Lagrange sense. In order to address the issue of non-commutative multiplication with respect to Clifford numbers, we divide the original n-dimensional Clifford-valued RNN model into $2^{m}n$
2
m
n
real-valued models. On the basis of Lyapunov stability theory and some analytical techniques, several sufficient conditions are obtained for the considered Clifford-valued RNN models to achieve global exponential stability according to the Lagrange sense. Two examples are presented to illustrate the applicability of the main results, along with a discussion on the implications.
2
m
n
real-valued models. On the basis of Lyapunov stability theory and some analytical techniques, several sufficient conditions are obtained for the considered Clifford-valued RNN models to achieve global exponential stability according to the Lagrange sense. Two examples are presented to illustrate the applicability of the main results, along with a discussion on the implications.
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Journal
Advances in Difference EquationsVolume
2021Issue
1Article number
ARTN 256Pagination
1 - 21Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
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2731-4235Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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