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H master-slave synchronization for delayed impulsive implicit hybrid neural networks based on memory-state feedback control.
Wang, Zekun; Zhuang, Guangming; Xie, Xiangpeng; Xia, Jianwei.
Afiliación
  • Wang Z; School of Mathematical Sciences, Liaocheng University, Liaocheng Shandong 252059, PR China.
  • Zhuang G; School of Mathematical Sciences, Liaocheng University, Liaocheng Shandong 252059, PR China. Electronic address: zgmtsg@126.com.
  • Xie X; Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, PR China.
  • Xia J; School of Mathematical Sciences, Liaocheng University, Liaocheng Shandong 252059, PR China.
Neural Netw ; 165: 540-552, 2023 Aug.
Article en En | MEDLINE | ID: mdl-37352598
ABSTRACT
This paper investigates the H∞ master-slave synchronization problem for delayed impulsive implicit hybrid neural networks based on memory-state feedback control. By developing a more holistic stochastic impulse-time-dependent Lyapunov-Krasovskii functional and dealing with the nonlinear neuron activation function, the stochastic admissibility and prescribed H∞ performance index for the synchronization error closed-loop system are achieved. In addition, the desired mode-dependent memory-state feedback synchronization controller is acquired in the form of linear matrix inequalities. The free-weighting matrix technique is adopted to remove the inherent limitation of time-varying delay derivative for the implicit delayed systems, and the derivative of time-varying delay is relaxed enough to be greater than 1. The simulation of genetic regulatory network in bio-economic system is given to verify validity of the derived results.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Reguladoras de Genes Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Reguladoras de Genes Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2023 Tipo del documento: Article