title: Complete delay-decomposing approach to asymptotic stability for neural networks with time-varying delays.
abstract: This paper is concerned with the problem of stability of neural networks with time-varying delays. A novel Lyapunov-Krasovskii functional decomposing the delays in all integral terms is proposed. By exploiting all possible information and considering independent upper bounds of the delay derivative in various delay intervals, some new generalized delay-dependent stability criteria are established, which are different from the existing ones and improve upon previous results. Numerical examples are finally given to demonstrate the effectiveness and the merits of the proposed method.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, China.
authors.forename: Hong-Bing
authors.initials: HB
authors.lastname: Zeng
authors.forename: Yong
authors.initials: Y
authors.lastname: He
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.forename: Chang-Fan
authors.initials: CF
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D011930:Reaction Time; D013997:Time Factors