title: Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays.
abstract: We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002).
authors.affiliation: Research Center of Modern Enterprise Management of Guilin University of Technology, Guilin University of Technology, Guilin 541004, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Lu
authors.affiliation: Research Center of Modern Enterprise Management of Guilin University of Technology, Guilin University of Technology, Guilin 541004, China.
authors.forename: Chaoling
authors.identifier: 0000-0002-7699-7391
authors.initials: C
authors.lastname: Li
mesh_terms: D006801:Humans; D008568:Memory / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010507:Periodicity