Name
Title | ||
|---|---|---|
Novel Global Asymptotic Stability Conditions for Hopfield Neural Networks with Time Delays |
Abstract | ||
|---|---|---|
In this paper, the global asymptotic stability of Hopfield neural networks with time delays is investigated. Some novel sufficient conditions are presented for the global stability of a given delayed Hopfield neural networks by constructing Lyapunov functional and using some well-known inequalities. A linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the given neural networks. An illustrative example is provided to demonstrate the effectiveness of our theoretical results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72383-7_109 | ISNN (1) |
Keywords | Field | DocType |
sufficient condition,neural network,illustrative example,novel global asymptotic stability,global stability,time delays,hopfield neural network,linear matrix inequality,theoretical result,hopfield neural networks,time delay,novel sufficient condition,global asymptotic stability,lyapunov function | Mathematical optimization,Computer science,Control theory,Recurrent neural network,Exponential stability,Artificial neural network,Cellular neural network,Lyapunov functional,Hopfield network,Linear matrix inequality | Conference |
Volume | ISSN | Citations |
4491 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Gao | 1 | 10 | 1.11 |
| Baotong Cui | 2 | 339 | 39.97 |
| Li Sheng | 3 | 125 | 15.24 |