- Pub. Date:
- Cambridge University Press
The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas. Applied Neural Networks for Signal Processing is the first book to provide 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 important 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 will be an invaluable 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.
|Publisher:||Cambridge University Press|
|Edition description:||New Edition|
|Product dimensions:||6.97(w) x 9.96(h) x 0.94(d)|
Table of Contents
1. Fundamental models of neural networks for signal processing; 2. Neural networks for filtering; 3. Neural networks for spectral estimation; 4. Neural networks for signal detection; 5. Neural networks for signal reconstruction; 6. Neural networks for adaptive extraction of principal and minor components; 7. Neural networks for array signal processing; 8. Neural networks for system identification; 9. Neural networks for signal compression.