Title :
Existence and exponential stability of periodic solution of discrete-time Cohen-Grossberg neural network with varying delays and impulses
Author :
Wang Lingzhi ; Qin Fajin
Author_Institution :
Dept. of Math. & Comput. Sci., Liuzhou Teachers Coll., Liuzhou, China
Abstract :
A class of the discrete-time Cohen-Grossberg neural network model is studied in this paper. By using the properties of ρ -cone and fixed point theorem, Some sufficient conditions to guarantee the uniqueness and global exponential stability of the periodic solution of such networks are established, and the estimated exponential convergence rate is also obtained. The results of this paper are new and they extend and improve previously known results.
Keywords :
convergence; delays; discrete time systems; neural nets; ρ-cone; discrete-time Cohen-Grossberg neural network; exponential convergence rate estimation; fixed point theorem; global exponential stability; periodic solution; varying delays; Artificial neural networks; Computational modeling; Convergence; Delay; Stability analysis; Discrete-time Cohen-Grossberg Neural Network; Exponential Stability; Fixed Point Theorem; Periodic Solution;
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768
