Title :
An analysis of global asymptotic stability of delayed Cohen-Grossberg neural networks via nonsmooth analysis
Author :
Yuan, Kun ; Cao, Jinde
Author_Institution :
Dept. of Math., Southeast Univ., Nanjing, China
Abstract :
In this paper, using a method based on nonsmooth analysis and the Lyapunov method, several new sufficient conditions are derived to ensure existence and global asymptotic stability of the equilibrium point for delayed Cohen-Grossberg neural networks. The obtained criteria can be checked easily in practice and have a distinguished feature from previous studies, and our results do not need the smoothness of the behaved function, boundedness of the activation function and the symmetry of the connection matrices. Moreover, two examples are exploited to illustrate the effectiveness of the proposed criteria in comparison with some existing results.
Keywords :
Lyapunov methods; asymptotic stability; circuit stability; matrix algebra; network analysis; neural nets; Lyapunov methods; delayed Cohen-Grossberg neural networks; global asymptotic stability; nonsmooth analysis; Asymptotic stability; Delay effects; Hopfield neural networks; Jacobian matrices; Lyapunov method; Neural networks; Neurons; Pattern recognition; Spectral analysis; Sufficient conditions; Cohen–Grossberg neural networks; Lyapunov functional; generalized Jacobian; global asymptotic stability; homeomorphism; nonsmooth analysis; spectral radius;
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
DOI :
10.1109/TCSI.2005.852210
