Copyright © 2010 Yongkun Li et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
By using coincidence degree theory and Lyapunov functions, we study the existence and global exponential stability
of antiperiodic solutions for a class of generalized neural networks with impulses and arbitrary delays on time scales.
Some completely new sufficient conditions are established. Finally, an example is given to illustrate our results.
These results are of great significance in designs and applications of globally stable anti-periodic Cohen-Grossberg neural
networks with delays and impulses.