Abstract
In this paper, we propose a new passive weight learning law for switched Hopfield neural networks with time-delay under parametric uncertainty. Based on the proposed passive learning law, some new stability results, such as asymptotical stability, input-to-state stability (ISS), and bounded input-bounded output (BIBO) stability, are presented. An existence condition for the passive weight learning law of switched Hopfield neural networks is expressed in terms of strict linear matrix inequality (LMI). Finally, numerical examples are provided to illustrate our results.
| Original language | English |
|---|---|
| Pages (from-to) | 4582-4594 |
| Number of pages | 13 |
| Journal | Information Sciences |
| Volume | 180 |
| Issue number | 23 |
| DOIs | |
| Publication status | Published - 2010 Dec 1 |
| Externally published | Yes |
Keywords
- Input-to-state stability (ISS)
- Linear matrix inequality (LMI)
- Lyapunov-Krasovskii stability theory
- Passive weight learning law
- Switched Hopfield neural networks
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence