Recurrent Neural Networks for Prediction
Language: English
Published by John Wiley & Sons Sep 2001, 2001
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - Durch die Anwendung rückbezüglicher neuronaler Netze läßt 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 faßt Originalarbeiten zur Stabilität neuronaler Netze zusammen und verbindet streng mathematische Analysen mit anschaulichen Anwendungen und experimentellen Belegen.
Seller Inventory # 9780471495178
- Title
- Recurrent Neural Networks for Prediction
- Author
- Mandic
- Publisher
- John Wiley & Sons Sep 2001
- Publication year
- 2001
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0471495174
- ISBN 13
- 9780471495178
- Item weight
- 716 grams
- Dimensions
- 250x175x21 mm
- Series
- Book 6 of 33: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control
- 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 RNNsPresents 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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"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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