Abstract
Cohen–Grossberg neural networks (CGNNs) play an important role in many applications and the stabilization of this system has been well studied. This study considers the exponential stabilization for stochastic reaction–diffusion Cohen–Grossberg neural networks (SRDCGNNs) by means of an aperiodically intermittent boundary control. Both SRDCGNNs without and with time-delays are discussed. By employing the spatial integral functional method and Poincare's inequality, criteria are derived to ensure the controlled systems achieve mean square exponential stabilization. Based on these criteria, the effects of diffusion item, control gains, the minimum control proportion and time-delays on exponential stability are analyzed. Examples are given to illustrate the effectiveness of the obtained theoretical results.
| Original language | English |
|---|---|
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | Neural Networks |
| Volume | 131 |
| DOIs | |
| State | Published - Nov 2020 |
| Externally published | Yes |
Keywords
- Aperiodically intermittent boundary control
- Cohen–Grossberg neural networks
- Exponential stability
- Stochastic reaction–diffusion systems
- Time-delays
Fingerprint
Dive into the research topics of 'Intermittent boundary stabilization of stochastic reaction–diffusion Cohen–Grossberg neural networks'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver