Global exponential synchronization of discrete-time high-order BAM neural networks with multiple time-varying delays - Er-yong Cong; Li Zhu; Xian Zhang (AIMS Mathematics)
The global exponential synchronization (GES) problem of a class of discrete-time high-order bidirectional associative memory neural networks (BAMNNs) with multiple time-varying delays (T-VDs) is studied. We investigate novel delay-dependent global exponential stability criteria for the error system by proposing a mathematical induction method. The global exponential stability criteria that have been obtained are described through linear scalar inequalities. These exponential synchronization conditions are very simple and convenient for verification based on standard software tools (such as YALMIP). Lastly, an instance is presented to demonstrate the validity of the theoretical findings.