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Item Details
Title: RECURRENT NEURAL NETWORKS FOR PREDICTION
LEARNING ALGORITHMS, ARCHITECTURES AND STABILITY
By: Danilo P. Mandic, Jonathon Chambers
Format: Hardback

List price: £174.95


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ISBN 10: 0471495174
ISBN 13: 9780471495178
Publisher: JOHN WILEY AND SONS LTD
Pub. date: 6 August, 2001
Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning,
Pages: 308
Description: Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain.
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 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! http://www.wiley.co.uk/commstech/ VISIT OUR WEB PAGE! http://www.wiley.co.uk/
Publication: UK
Imprint: John Wiley & Sons Ltd
Returns: Returnable
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