Stability Analysis of Delayed Neural Networks via Composite-Matrix-Based Integral Inequality
This paper revisits the problem of stability analyses for neural networks with time-varying delay. A composite-matrix-based integral inequality (CMBII) is presented, which takes the delay derivative into account. In this case, the coupling information can be fully captured in integral inequalities w...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/11/2518 |
| Summary: | This paper revisits the problem of stability analyses for neural networks with time-varying delay. A composite-matrix-based integral inequality (CMBII) is presented, which takes the delay derivative into account. In this case, the coupling information can be fully captured in integral inequalities with the delay derivative. Based on a CMBII, a new stability criterion is derived for neural networks with time-varying delay. The effectiveness of this method is verified by a numerical example. |
|---|---|
| ISSN: | 2227-7390 |