Global exponential stability of impulsive high-order BAM neural networks with time-varying delays.
In this paper, global exponential stability and exponential convergence are studied for a class of impulsive high-order bidirectional associative memory (BAM) neural networks with time-varying delays. By employing linear matrix inequalities (LMIs) and differential inequalities with delays and impulses, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Three illustrative examples are also given at the end of this paper to show the effectiveness of our results.
Department of Mathematics, City University of Hong Kong, 83 Tat Chee Ave., Hong Kong.
Daniel W C
DW
Ho
Jinling
J
Liang
James
J
Lam
D000465:Algorithms; D006801:Humans; D016014:Linear Models; D008568:Memory / Q000502:physiology*; D009415:Nerve Net; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D013997:Time Factors