Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
| AUTHOR | Mandic; Mandic; Mandic et al. |
| PUBLISHER | Wiley (09/05/2001) |
| PRODUCT TYPE | Hardcover (Hardcover) |
Description
Durch die Anwendung rückbezüglicher neuronaler Netze lä t sich die Leistungsfähigkeit konventioneller Technologien der digitalen Datenverarbeitung signifikant erhöhen. Von besonderer Bedeutung ist dies für komplexe Aufgaben, wie z.B. die mobile Kommunikation, die Robotik und die Medizintechnik. Das Buch fa t Originalarbeiten zur Stabilität neuronaler Netze zusammen und verbindet streng mathematische Analysen mit anschaulichen Anwendungen und experimentellen Belegen.
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Product Format
Product Details
ISBN-13:
9780471495178
ISBN-10:
0471495174
Binding:
Hardback or Cased Book (Sewn)
Content Language:
English
More Product Details
Page Count:
304
Carton Quantity:
22
Product Dimensions:
6.06 x 0.87 x 9.77 inches
Weight:
1.24 pound(s)
Country of Origin:
GB
Subject Information
BISAC Categories
Computers | Data Science - Neural Networks
Computers | Networking - General
Dewey Decimal:
006.32
Library of Congress Control Number:
2001033418
Descriptions, Reviews, Etc.
jacket back
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.
- Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting
- Examines stability and relaxation within RNNs
- Presents on-line learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, data-reusing adaptation, and normalisation
- Studies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration
- Describes strategies for the exploitation of inherent relationships between parameters in RNNs
- Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing
Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.
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publisher marketing
Durch die Anwendung rückbezüglicher neuronaler Netze lä t sich die Leistungsfähigkeit konventioneller Technologien der digitalen Datenverarbeitung signifikant erhöhen. Von besonderer Bedeutung ist dies für komplexe Aufgaben, wie z.B. die mobile Kommunikation, die Robotik und die Medizintechnik. Das Buch fa t Originalarbeiten zur Stabilität neuronaler Netze zusammen und verbindet streng mathematische Analysen mit anschaulichen Anwendungen und experimentellen Belegen.
Show More
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