Global Exponential Stability and Periodicity of Nonautonomous Impulsive Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms | Zendy

Weiyi Hu | Zendy; Kelin Li | Zendy

Open Access

Global Exponential Stability and Periodicity of Nonautonomous Impulsive Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Author(s) -

Weiyi Hu,

Kelin Li

Publication year - 2021

Publication title -

complexity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.447

H-Index - 61

eISSN - 1099-0526

pISSN - 1076-2787

DOI - 10.1155/2021/3495545

Subject(s) - exponential stability , reaction–diffusion system , mathematics , stability (learning theory) , artificial neural network , exponential function , fixed point theorem , exponential growth , diffusion , cellular neural network , mathematical analysis , control theory (sociology) , computer science , physics , nonlinear system , control (management) , quantum mechanics , machine learning , artificial intelligence , thermodynamics

In this paper, we investigate the global exponential stability and periodicity of nonautonomous cellular neural networks with reaction-diffusion, impulses, and time-varying delays. By establishing a new differential inequality for nonautonomous systems, using the properties of M-matrix and inequality techniques, some new sufficient conditions for the global exponential stability of the system are obtained. Moreover, sufficient conditions for the periodic solutions of the system are obtained by using the Poincare mapping and the fixed point theory. The validity and superiority of the main results are verified by numerical examples and simulations.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research