title: A new delay-independent condition for global robust stability of neural networks with time delays.
abstract: This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bounded. By employing the results of Lyapunov stability theory and matrix theory, new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for delayed neural networks are presented. The results reported in this paper can be easily tested by checking some special properties of symmetric matrices associated with the parameter uncertainties of neural networks. We also present a numerical example to show the effectiveness of the proposed theoretical results.
authors.affiliation: Istanbul University, Department of Computer Engineering, 34320 Avcilar, Istanbul, Turkey. Electronic address: ruyasamli@istanbul.edu.tr.
authors.forename: Ruya
authors.initials: R
authors.lastname: Samli
mesh_terms: D016571:Neural Networks, Computer; D013997:Time Factors; D035501:Uncertainty
keywords: Delayed systems; Lyapunov functionals; Neural networks; Robust stability