Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and StabilityWiley, 5 sep 2001 - 308 pagina's 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.
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! |
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Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures ... Danilo P. Mandic,Jonathon A. Chambers Geen voorbeeld beschikbaar - 2001 |
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