title: Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays.
abstract: In this paper, a class of recurrent neural networks with multi-proportional delays is studied. The nonlinear transformation transforms a class of recurrent neural networks with multi-proportional delays into a class of recurrent neural networks with constant delays and time-varying coefficients. By constructing Lyapunov functional and establishing the delay differential inequality, several delay-dependent and delay-independent sufficient conditions are derived to ensure global exponential periodicity and stability of the system. And several examples and their simulations are given to illustrate the effectiveness of obtained results.
authors.affiliation: School of Mathematics Science, Tianjin Normal University, Tianjin 300387, China. Electronic address: liqunzhou@mail.tjnu.edu.cn.
authors.forename: Liqun
authors.initials: L
authors.lastname: Zhou
authors.affiliation: School of Mathematics Science, Tianjin Normal University, Tianjin 300387, China.
authors.forename: Yanyan
authors.initials: Y
authors.lastname: Zhang
keywords: Global exponential periodicity; Global exponential stability; Nonlinear transformation; Proportional delay factor; Recurrent neural networks (RNNs)