title: Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects.
abstract: This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.
authors.forename: Tingwen
authors.initials: T
authors.lastname: Huang
authors.forename: Chuandong
authors.initials: C
authors.lastname: Li
authors.forename: Shukai
authors.initials: S
authors.lastname: Duan
authors.forename: Janusz A
authors.initials: JA
authors.lastname: Starzyk
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes