Recurrent neural networks for prediction
Danilo P Mandic, Jonathon Chambers
284 pages, parution le 01/08/2001
Résumé
From mobile communications to robotics to space technology
to medical instrumentation, new technologies are demanding
increasingly complex methods of digital signal processing
(DSP). This book shows researchers how recurrent neural
networks can be implemented to expand the range of
traditional signal processing techniques. Featuring
original research on stability in neural networks, the book
combines rigorous mathematical analysis with application
examples. Experimental evidence as well as an overview of
existing approaches are also included.
Table of Contents
- I: RECURRENT NEURAL NETWORKS FOR PREDICTION.
- 1. Neural networks for prediction-perspective.
- 2. NARMA recurrent neural networks.
- II: A POSTERIORI LEARNING IN RECURRENT NEURAL NETWORKS FOR PREDICTION.
- 3. A posteriori recurrent neural networks.
- 4. On the a priori adaptation a posteriori error algorithm.
- 5. A full a posteriori learning algorithm.
- III: NESTED RNN ARCHITECTURES—THE PIPELINED RECURRENT NEURAL NETWORK.
- 6. The pipelined recurrent neural network.
- 7. Towards an optimal PRNN.
- IV: STABILITY ISSUES IN RNN ARCHITECTURES.
- 8. A fixed point interpretation of convergence in networks with a sigmoid nonlinearity.
- 9. Global asymptotic convergence of nonlinear relaxation equations realised through a recurrent perception.
- 10. Stability of nonlinear NARMA relaxation for RNN architectures.
- V: EXPLOITING INHERENT RELATIONSHIPS BETWEEN PARAMETERS IN RECURRENT NEURAL NETWORKS.
- 11. Relationship between n and B for RNNs.
- 12. Relationship between n and B for the PRNN.
- 13. Conclusions.
- Appendices.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Wiley |
| Auteur(s) | Danilo P Mandic, Jonathon Chambers |
| Parution | 01/08/2001 |
| Nb. de pages | 284 |
| Format | 17 x 25 |
| Couverture | Relié |
| Poids | 725g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780471495178 |
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