Stability of stochastic impulsive reaction–diffusion neural networks with S-type distributed delays and its application to image encryption. (August 2019)

• Record Type: Journal Article
• Authors: Wei, Tengda; Lin, Ping; Wang, Yangfan; Wang, Linshan
• Is Part Of: Neural networks. Volume 116(2019)
• Page Start: 35
• Page End: 45
• Publication Date: 2019-08
• Subjects: Stochastic reaction–diffusion neural network -- Impulse -- S-type distributed delay -- Image encryption; 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.2019.03.016
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, we study stochastic impulsive reaction–diffusion neural networks with S-type distributed delays, aiming to obtain the sufficient conditions for global exponential stability. First, an impulsive inequality involving infinite delay is introduced and the asymptotic behaviour of its solution is investigated by the truncation method. Then, global exponential stability in the mean-square sense of the stochastic impulsive reaction–diffusion system is studied by constructing a simple Lyapunov–Krasovskii functional where the S-type distributed delay is handled by the impulsive inequality. Numerical examples are also given to verify the effectiveness of the proposed results. Finally, the obtained theoretical results are successfully applied to an image encryption scheme based on bit-level permutation and the stochastic neural networks.