Recurrent Neural Networks for Prediction, 0003rd Edition
learning algorithms, architectures and stability
$530.77
- Hardcover
304 pages
- Release Date
5 August 2001
Summary
Mastering Time: Recurrent Neural Networks for Predictive Power
New technologies in engineering, physics, and biomedicine demand increasingly complex methods of digital signal processing. This book showcases the latest research demonstrating how real-time recurrent neural networks (RNNs) can expand traditional signal processing techniques and tackle the challenge of prediction. Here, neural networks are viewed as massively interconnected nonlinear adaptive filters.
- Anal…
Book Details
| ISBN-13: | 9780471495178 |
|---|---|
| ISBN-10: | 0471495174 |
| Author: | Danilo P. Mandic, Jonathon A. Chambers |
| Publisher: | John Wiley & Sons Inc |
| Imprint: | John Wiley & Sons Inc |
| Format: | Hardcover |
| Number of Pages: | 304 |
| Edition: | 0003rd |
| Release Date: | 5 August 2001 |
| Weight: | 709g |
| Dimensions: | 247mm x 174mm x 23mm |
| Series: | Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control |
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About The Author
Danilo P. Mandic
Danilo Mandic from the Imperial College London, London, UK was named Fellow of the Institute of Electrical and Electronics Engineers in 2013 for contributions to multivariate and nonlinear learning systems.
Jonathon A. Chambers is the author of Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, published by Wiley.
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