Recurrent Neural Networks for Prediction (Hardcover)

Danilo P. Mandic

ISBN 10: 0471495174 / ISBN 13: 9780471495178

Published by John Wiley & Sons Inc, New York, 2001

About this Item

Hardcover. 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 nestingExamines 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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. VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! VISIT OUR WEB PAGE! / Neural networks consist of interconnected groups of neurones which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced to meet the demands of new technologies such as mobile communications, robotics and medical instrumentation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780471495178

Bibliographic Details

• Title: Recurrent Neural Networks for Prediction (Hardcover)
• Publisher: John Wiley & Sons Inc, New York
• Publication Date: 2001
• Language: English
• ISBN 10: 0471495174
• ISBN 13: 9780471495178
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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 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.

VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE!
http://www.wiley.co.uk/commstech/

VISIT OUR WEB PAGE!
http://www.wiley.co.uk/

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.