Stability Analysis for Delayed Neural Networks via a Novel Negative-Definiteness Determination Method.
IEEE Trans Cybern
; 52(6): 5356-5366, 2022 Jun.
Article
en En
| MEDLINE
| ID: mdl-33201831
ABSTRACT
The stability of neural networks with a time-varying delay is studied in this article. First, a relaxed Lyapunov-Krasovskii functional (LKF) is presented, in which the positive-definiteness requirement of the augmented quadratic term and the delay-product-type terms are set free, and two double integral states are augmented into the single integral terms at the same time. Second, a new negative-definiteness determination method is put forward for quadratic functions by utilizing Taylor's formula and the interval-decomposition approach. This method encompasses the previous negative-definiteness determination approaches and has less conservatism. Finally, the proposed LKF and the negative-definiteness determination method are applied to the stability analysis of neural networks with a time-varying delay, whose advantages are shown by two numerical examples.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Cybern
Año:
2022
Tipo del documento:
Article