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Exponential stability in the Lagrange sense for Clifford-valued recurrent neural networks with time delays

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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 Agarwal
AbstractThis 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.

History

Journal

Advances in Difference Equations

Volume

2021

Article number

ARTN 256

Pagination

1 - 21

Location

Berlin, Germany

Open access

  • Yes

ISSN

1687-1839

eISSN

1687-1847

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

1

Publisher

SPRINGER