Abstract
Global exponential stability is a desirable property for dynamic systems. This paper studies the global exponential stability of several existing recurrent neural networks for solving linear programming problems, convex programming problems with interval constraints, convex programming problems with nonlinear constraints, and monotone variational inequalities. In contrast to the existing results on global exponential stability, the present results do not require additional conditions on the weight matrices of recurrent neural networks and improve some existing conditions for global exponential stability. Therefore, the stability results in this paper further demonstrate the superior convergence properties of the existing neural networks for optimization.
| Original language | English |
|---|---|
| Pages (from-to) | 1017-1022 |
| Number of pages | 6 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 11 |
| Issue number | 4 |
| Online published | 31 Jul 2000 |
| DOIs | |
| Publication status | Published - Jul 2000 |
| Externally published | Yes |
Research Keywords
- Global exponential stability
- optimization problems
- recurrent neural networks
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