Abstract
Global exponential stability is most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements. © 2002 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 633-644 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 13 |
| Issue number | 3 |
| Online published | 31 May 2002 |
| DOIs | |
| Publication status | Published - May 2002 |
| Externally published | Yes |
Research Keywords
- Global exponential stability
- Pole assignment
- Recurrent neural networks
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