title: Relaxed stability conditions for delayed recurrent neural networks with polytopic uncertainties.
abstract: This paper investigates the problem of stability analysis for recurrent neural networks with time-varying delays and polytopic uncertainties. Parameter-dependent Lypaunov functionals are employed to obtain sufficient conditions that guarantee the robust global exponential stability of the equilibrium point of the considered neural network. The derived stability criteria are expressed in terms of a set of relaxed linear matrix inequalities, which can be easily tested by using commercially available software. Two numerical examples are provided to demonstrate the effectiveness of the proposed results.
authors.affiliation: Department of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, PR China. baoyongzhang@yahoo.com.cn
authors.forename: Baoyong
authors.initials: B
authors.lastname: Zhang
authors.forename: Shengyuan
authors.initials: S
authors.lastname: Xu
authors.forename: Yun
authors.initials: Y
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors