Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6857857 | Information Sciences | 2014 | 16 Pages |
Abstract
In this paper, we study the existence, uniqueness and stability of periodic solution for a wide class of memristor-based neural networks with time-varying delays. By employing the topological degree theory in set-valued analysis, differential inclusions theory and a new Lyapunov function method, we prove that the neural network has a unique periodic solution, which is globally exponentially stable. Moreover, we prove the existence, uniqueness and global exponential stability of equilibrium point for time-varying delayed memristor-based neural networks with constant coefficients. The obtained results improve and extend previous works on memristor-based or usual neural network dynamical systems with continuous or discontinuous right-hand side. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jiejie Chen, Zhigang Zeng, Ping Jiang,