Name
Title | ||
|---|---|---|
Local exponential stability of competitive neural networks with different time scales |
Abstract | ||
|---|---|---|
This contribution presents a new method of analyzing the dynamics of a biological relevant neural network with different time scales based on the theory of flow invariance. We are able to show that the resulting stability conditions are less restrictive and more general than with K-monotone theory or singular perturbation theory. The theoretical results are further substantiated by simulation results conducted for analysis and design of these neural networks. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.engappai.2004.02.010 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
neural network,exponential stability | Mathematical optimization,Invariant (physics),Computer science,Flow (psychology),Stability conditions,Singular perturbation,Exponential stability,Artificial neural network,Monotone polygon | Journal |
Volume | Issue | ISSN |
17 | 3 | 0952-1976 |
Citations | PageRank | References |
17 | 0.92 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anke Meyer-Bäse | 1 | 180 | 19.36 |
| Sergei S. Pilyugin | 2 | 32 | 7.31 |
| Axel Wismüller | 3 | 324 | 35.91 |
| Simon Foo | 4 | 43 | 4.10 |