Global Exponential Stability of Antiperiodic Solutions for Discrete‐Time Neural Networks with Mixed Delays and Impulses
Xiaofeng Chen, Qiankun Song · Discrete Dynamics in Nature and Society · 2012
The problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete‐time neural networks with time‐varying discrete delays and distributed delays. By constructing an appropriate Lyapunov‐Krasovskii functional, and using the contraction mapping principle and the matrix inequality techniques, a new delay‐dependent criterion for checking the existence, uniqueness, and global exponential stability of anti‐periodic solution is derived in linear matrix inequalities (LMIs). Two simulation examples are given to show the effectiveness of the proposed result.