Name
Abstract | ||
|---|---|---|
The problem of stability analysis for neural networks (NNs) with interval time-varying delay is investigated. New delay-decomposing approaches which are dividing the variation interval of the delay into two unequal subintervals are proposed. Some new simple Lyapunov–Krasovskii functionals (LKFs) are defined on the obtained subintervals. The integral inequality method and the reciprocally convex technique are utilized to deal with the derivative of the LKFs. Several improved delay-dependent criteria are derived in terms of the linear matrix inequalities (LMIs). Compared with some previous criteria, the proposed ones give the results with less conservatism and lower numerical complexity. Two numerical examples are included to illustrate the effectiveness and the improvement of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.12.088 | Neurocomputing |
Keywords | Field | DocType |
Neural network,Stability,Delay-decomposing approach,Time-varying delay,LMI | LKFS,Mathematical optimization,Division (mathematics),Matrix (mathematics),Regular polygon,Artificial neural network,Mathematics | Journal |
Volume | ISSN | Citations |
189 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangdong Guo | 1 | 36 | 3.53 |
| Xiqin He | 2 | 7 | 0.80 |
| Jianjun He | 3 | 10 | 5.59 |