Name
Abstract | ||
|---|---|---|
In this note, the global asymptotic stability of a class of delayed cellular neural networks is studied. Some new sufficient conditions are presented for the uniqueness of equilibrium point and the global stability of cellular neural networks with time varying delay by constructing Lyapunov functional and using linear matrix inequality (LMI). A numerical example is presented to illustrate the effectiveness of our theoretical results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.537 | ICNC |
Keywords | Field | DocType |
theoretical result,delayed cellular neural networks,asymptotic stability,global asymptotic stability,time-varying systems,delayed cellular neural network,time varying delay,varying delay,linear matrix inequality,cellular neural networks,delays,cellular neural network,cellular neural nets,linear matrix inequalities,equilibrium point,numerical example,new sufficient condition,global stability,artificial neural networks,numerical stability,stability analysis | Uniqueness,Mathematical optimization,Control theory,Computer science,Equilibrium point,Exponential stability,Artificial neural network,Cellular neural network,Lyapunov functional,Numerical stability,Linear matrix inequality | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3304-9 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pu Wang | 1 | 25 | 10.26 |
| Xue li Wu | 2 | 11 | 5.11 |
| Ran Zhen | 3 | 18 | 4.12 |
| Yanhong Wang | 4 | 8 | 2.84 |