Global exponential convergence of Cohen-Grossberg neural networks with time delays

Author

Lu, Hongtao ; Shen, Ruiming ; Chung, Fu-lai

Author_Institution

Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China

Volume

16

Issue

6

fYear

2005

Firstpage

1694

Lastpage

1696

Abstract

In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.

Keywords

Lyapunov methods; asymptotic stability; convergence; delays; matrix algebra; neural nets; numerical stability; Cohen-Grossberg neural network; Lyapunov functional; column-sum; global exponential convergence; network coefficient; row-sum; time delay; Associative memory; Cellular neural networks; Convergence; Delay effects; Delay estimation; Hopfield neural networks; Neural networks; Neurons; Stability; Sufficient conditions; Cohen–Grossberg neural networks; global exponential stability; time delay; Algorithms; Computer Simulation; Models, Theoretical; Neural Networks (Computer); Numerical Analysis, Computer-Assisted; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2005.853336

Filename

1528544