Name
Abstract | ||
|---|---|---|
In this paper, we handle the problem of adaptive control of neural network systems subjected to time-varying delay. First of all, a simple adaptive control strategy is designed by combining adaptive scheme and linear feedback with the updated feedback strength, which is strictly proved in the framework of Krasovskii-Lyapunov theory and is valid for generic high-dimensional nonlinear systems containing constant or time-varying delay. By the proposed method, the controlled orbit of a Hopfield neural network system containing time-varying delay can track the target orbit quickly, which verifies the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICNC.2010.5583987 | ICNC |
Keywords | Field | DocType |
neural network,hopfield neural nets,linear feedback,neurocontrollers,time-varying systems,time-varying delay,krasovskii-lyapunov theory,generic high-dimensional nonlinear systems,delays,hopfield neural network system,feedback,adaptive control,nonlinear control systems,lyapunov methods,nonlinear system,control systems | Mathematical optimization,Nonlinear system,Computer science,Control theory,Time delay neural network,Adaptive control,Control system,Artificial neural network,Neural network system | Conference |
Volume | ISBN | Citations |
7 | 978-1-4244-5958-2 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongkui Sun | 1 | 4 | 5.99 |
| Xiaoli Yang | 2 | 1 | 2.42 |