Impulsive synchronization of stochastic reaction–diffusion neural networks with mixed time delays. (July 2018)

• Record Type: Journal Article
• Authors: Sheng, Yin; Zeng, Zhigang
• Is Part Of: Neural networks. Volume 103(2018)
• Page Start: 83
• Page End: 93
• Publication Date: 2018-07
• Subjects: Stochastic reaction–diffusion neural networks -- Mixed time delays -- Synchronization -- Impulse -- Stochastic analysis; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.03.010
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper discusses impulsive synchronization of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions and hybrid time delays. By virtue of inequality techniques, theories of stochastic analysis, linear matrix inequalities, and the contradiction method, sufficient criteria are proposed to ensure exponential synchronization of the addressed stochastic reaction–diffusion neural networks with mixed time delays via a designed impulsive controller. Compared with some recent studies, the neural network models herein are more general, some restrictions are relaxed, and the obtained conditions enhance and generalize some published ones. Finally, two numerical simulations are performed to substantiate the validity and merits of the developed theoretical analysis.