Abstract
A class of discrete-time recurrent neural networks for solving quadratic optimization problems over bound constraints is studied. The regularity and completeness of the network are discussed. The network is proven to be globally exponentially stable (GES) under some mild conditions. The analysis of GES extends the existing stability results for discrete-time recurrent networks. A simulation example is included to validate the theoretical results obtained in this letter.
| Original language | English |
|---|---|
| Pages (from-to) | 399-406 |
| Journal | Neurocomputing |
| Volume | 56 |
| Online published | 5 Aug 2003 |
| DOIs | |
| Publication status | Published - Jan 2004 |
| Externally published | Yes |
Research Keywords
- Discrete-time neural networks
- Global exponential stability
- Quadratic optimization
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