title: Improved delay-dependent stability analysis for neural networks with time-varying delays.
abstract: In this paper, the problem of delay-dependent asymptotic stability analysis for neural networks with time-varying delays is considered. A new class of Lyapunov functional is proposed by considering the information of neuron activation functions adequately. By using the delay-partitioning method and the reciprocally convex technique, some less conservative stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the effectiveness of the derived method.
authors.affiliation: School of Sciences, Southwest Petroleum University, Chengdu, Sichuan 610500, China. Electronic address: xiangjunxie1@163.com.
authors.forename: Xiangjun
authors.initials: X
authors.lastname: Xie
authors.affiliation: School of Sciences, Southwest Petroleum University, Chengdu, Sichuan 610500, China. Electronic address: zerongren@163.com.
authors.forename: Zerong
authors.initials: Z
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted
keywords: Delay-dependent stability; Linear matrix inequality (LMI); Neural networks; Time-varying delays