Abstract
Aiming at the problem of poor extensibility of feed-forward neural networks, a knowledge extension method is proposed in this paper. Preserving the original neural networks, we can both retain existing training result and learn new knowledge by adding a new subnet. Simultaneously, the strategy of fault recovery of neural networks is studied and a fault compensation algorithm is given. The effectiveness of proposed algorithms is verified by numerical simulations.
| Translated title of the contribution | Knowledge extension and fault recovery of feed-forward neural networks |
|---|---|
| Original language | Chinese (Simplified) |
| Pages (from-to) | 189-192 |
| Journal | 控制理论与应用/Control Theory & Applications |
| Volume | 17 |
| Issue number | 2 |
| Publication status | Published - Apr 2000 |
Research Keywords
- 前馈网
- 学习
- 故障补偿
- FNN
- Learning
- Fault compensation
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