title: Global convergence of delayed neural network systems.
abstract: In this paper, without assuming the boundedness, strict monotonicity and differentiability of the activation functions, we utilize a new Lyapunov function to analyze the global convergence of a class of neural networks models with time delays. A new sufficient condition guaranteeing the existence, uniqueness and global exponential stability of the equilibrium point is derived. This stability criterion imposes constraints on the feedback matrices independently of the delay parameters. The result is compared with some previous works. Furthermore, the condition may be less restrictive in the case that the activation functions are hyperbolic tangent.
authors.affiliation: Laboratory of Nonlinear Science, Institute of Mathematics, Fudan University, Shanghai, 200433, P.R. China.
authors.forename: Wenlian
authors.initials: W
authors.lastname: Lu
authors.forename: Libin
authors.initials: L
authors.lastname: Rong
authors.forename: Tianping
authors.initials: T
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013997:Time Factors