title: Improved stability criteria of static recurrent neural networks with a time-varying delay.
abstract: This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.
authors.affiliation: School of Information Science and Engineering, Jishou University, Jishou 416000, China.
authors.forename: Lei
authors.identifier: 0000-0001-8352-7139
authors.initials: L
authors.lastname: Ding
authors.affiliation: School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China.
authors.forename: Hong-Bing
authors.initials: HB
authors.lastname: Zeng
authors.affiliation: Hunan Railway Professional Technology College, Zhuzhou 412001, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Jiangsu Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Soochow 215006, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer