Abstract
A new generalized cost criterion based learning algorithm for diagonal recurrent neural networks is presented, which is with form of recursive prediction error (RPE) and has second convergent order. A guideline for the choice of the optimal learning rate is derived from convergence analysis. The application of this method to dynamic modeling of typical chemical processes shows that the generalized cost criterion RPE (QRPE) has higher modeling precision than BP trained MLP and quadratic cost criterion trained RPE (QRPE).
| Original language | English |
|---|---|
| Pages (from-to) | 482-485 |
| Number of pages | 4 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 4077 |
| State | Published - 2000 |
| Externally published | Yes |
| Event | International Conference on Sensors and Control Techniques (ICSC 2000) - Wuhan, China Duration: Jun 19 2000 → Jun 21 2000 |