Applied neural networks for signal processing
378 pages, parution le 11/03/1999
Résumé
The use of neural networks in signal processing is becoming
increasingly widespread, with applications in many areas.
This book provides a comprehensive introduction to this
broad field. It begins by covering the basic principles and
models of neural networks in signal processing. The authors
then discuss a number of powerful algorithms and
architectures for a range of problems, and describe
practical implementation procedures. A key feature of the
book is that many carefully designed simulation examples
are included to help guide the reader in the development of
systems for new applications. The book should be a valuable
reference for scientists and engineers working in
communications, control or any other field related to
signal processing. It can also be used as a textbook for
graduate courses in electrical engineering and computer
science.
Summary of contents
- Fundamental models of neural networks for signal processing
- neural networks for filtering
- neural networks for spectral estimation
- neural networks for signal detection
- neural networks for signal reconstruction
- neural networks for adaptive extraction of principal and minor components
- neural networks for array signal processing
- neural networks for system identification
- neural networks for signal compression
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Cambridge University Press |
| Auteur(s) | Fa-Long Luo, Rolf Unbehauen |
| Parution | 11/03/1999 |
| Nb. de pages | 378 |
| Format | 17,8 x 25,5 |
| EAN13 | 9780521644006 |
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