Abstract
In this paper, an input-to-state stability (ISS) approach is introduced to derive a new state estimation filter for Hopfield neural networks with noise disturbance. A new ISS filtering method is developed such that the filtering error system is exponentially stable and input-to-state stable for the noise disturbance. The proposed filter can be obtained by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.
| Original language | English |
|---|---|
| Pages (from-to) | 275-278 |
| Number of pages | 4 |
| Journal | Advanced Science Letters |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2012 Jan 1 |
| Externally published | Yes |
Keywords
- Hopfield neural networks
- Input-to-state stability (ISS)
- Linear matrix inequality (LMI)
- Noise disturbance
- State estimation filter
ASJC Scopus subject areas
- Education
- Health(social science)
- Mathematics(all)
- Energy(all)
- Computer Science(all)
- Environmental Science(all)
- Engineering(all)