title: Stability of Recurrent Neural Networks With Time-Varying Delay via Flexible Terminal Method.
abstract: This brief is concerned with the stability criteria for recurrent neural networks with time-varying delay. First, based on convex combination technique, a delay interval with fixed terminals is changed into the one with flexible terminals, which is called flexible terminal method (FTM). Second, based on the FTM, a novel Lyapunov-Krasovskii functional is constructed, in which the integral interval associated with delayed variables is not fixed. Thus, the FTM can achieve the same effect as that of delay-partitioning method, while their implementary ways are different. Guided by FTM, Wirtinger-based integral inequality and free-weight matrix method are employed to develop several stability criteria, respectively. Finally, the feasibility and the effectiveness of the proposed results are tested by two numerical examples.
authors.forename: Zhanshan
authors.initials: Z
authors.lastname: Wang
authors.forename: Sanbo
authors.initials: S
authors.lastname: Ding
authors.forename: Qihe
authors.initials: Q
authors.lastname: Shan
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang