Name
Title | ||
|---|---|---|
Simplified exponential stability analysis for recurrent neural networks with discrete and distributed time-varying delays |
Abstract | ||
|---|---|---|
This paper provides simplified exponential stability criteria for a class of recurrent neural networks (RNNs) with discrete and distributed time-varying delays. The activation functions of the RNNs are assumed to be more general, and the proposed criteria are obtained by only using a integral inequality and are not involved any free-weighting matrices. This feature makes the computational burden largely reduced. Numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.amc.2008.08.022 | Applied Mathematics and Computation |
Keywords | Field | DocType |
Neural networks (NNs),Exponential stability,Delay-dependent,Linear matrix inequality (LMI) | Mathematical optimization,Matrix (mathematics),Recurrent neural network,Exponential stability,Discrete time and continuous time,Artificial neural network,Numerical analysis,Numerical stability,Mathematics | Journal |
Volume | Issue | ISSN |
205 | 1 | 0096-3003 |
Citations | PageRank | References |
18 | 0.92 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjiang Yu | 1 | 58 | 4.27 |
| Kan-Jian Zhang | 2 | 133 | 19.66 |
| Shu-Min Fei | 3 | 1150 | 96.93 |
| Tao Li | 4 | 33 | 2.68 |