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Stability and synchronization of fractional-order reaction-diffusion inertial time-delayed neural networks with parameters perturbation.
Wang, Hu; Gu, Yajuan; Zhang, Xiaoli; Yu, Yongguang.
Afiliación
  • Wang H; School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, 100081, China.
  • Gu Y; School of Applied Science, Beijing Information Science and Technology University, Beijing, 100192, China.
  • Zhang X; School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China.
  • Yu Y; School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China. Electronic address: ygyu@bjtu.edu.cn.
Neural Netw ; 179: 106564, 2024 Nov.
Article en En | MEDLINE | ID: mdl-39089150
ABSTRACT
This study is centered around the dynamic behaviors observed in a class of fractional-order generalized reaction-diffusion inertial neural networks (FGRDINNs) with time delays. These networks are characterized by differential equations involving two distinct fractional derivatives of the state. The global uniform stability of FGRDINNs with time delays is explored utilizing Lyapunov comparison principles. Furthermore, global synchronization conditions for FGRDINNs with time delays are derived through the Lyapunov direct method, with consideration given to various feedback control strategies and parameter perturbations. The effectiveness of the theoretical findings is demonstrated through three numerical examples, and the impact of controller parameters on the error system is further investigated.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos