title: Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach.
abstract: For neural networks with constant or time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are studied in this paper. An approach combining the Lyapunov-Krasovskii functionals with the linear matrix inequality is taken to investigate the problems, which provide bounds on the interconnection matrix and the activation functions, so as to guarantee the systems' exponential stability. Some criteria for the exponentially stability, which give information on the delay-dependence property, are derived. The results obtained in this paper provide one more set of easily verified guidelines for determining the exponentially stability of delayed neural networks, which are less conservative and less restrictive than the ones reported so far in the literature.
authors.affiliation: Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, People's Republic of China.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Liao
authors.forename: Guanrong
authors.initials: G
authors.lastname: Chen
authors.forename: Edgar N
authors.initials: EN
authors.lastname: Sanchez
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors