| Autor: |
G. Rajchakit, R. Sriraman, N. Boonsatit, P. Hammachukiattikul, C. P. Lim, P. Agarwal |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Advances in Difference Equations, Vol 2021, Iss 1, Pp 1-21 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1687-1847 |
| DOI: |
10.1186/s13662-021-03415-8 |
| Popis: |
Abstract This 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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