Exponential synchronization for reaction-diffusion neural networks with mixed time-varying delays via periodically intermittent control

Abstract

This paper deals with the exponential synchronization problem for reaction-diffusion neural networks with mixed time-varying delays and stochastic disturbance. By using stochastic analysis approaches and constructing a novel Lyapunov–Krasovskii functional, a periodically intermittent controller is first proposed to guarantee the exponential synchronization of reaction-diffusion neural networks with mixed time-varying delays and stochastic disturbance in terms of p-norm. The obtained synchronization results are easy to check and improve upon the existing ones. Particularly, the traditional assumptions on control width and time-varying delays are removed in this paper. This paper also presents two illustrative examples and uses simulated results of these examples to show the feasibility and effectiveness of the proposed scheme.

Aperiodically intermittent stochastic stabilization via discrete time or delay feedback control

“…Moreover, intermittent control technology has recently attracted the attention of several scholars [27][28][29][30][31][32][33][34][35][36][37]. Compared to the classic continuous control strategy, the intermittent control strategy is more economical and can simulate the real world better.…”

Coexistence of generalized synchronization and inverse generalized synchronization between chaotic and hyperchaotic systems

“…The study of nonlinear behaviour, such as stability, control and synchronization of dynamical systems, has become an interdisciplinary research. As a result, several research works have been published on this subject by researchers of different disciplines [5,6,9,34]. Due to the importance and interdisciplinary nature of nonlinear dynamical systems, its applications have been discovered in field studies like mathematics, physics, chemistry, engineering, economics, neuroscience and many more [7,8,10,11,13,33,37].…”