Title :
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
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.853336
