Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. Get and download textbook Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability for free
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 techn
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 Recurrent Neural Networks for Prediction new edition
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Recurrent neural networks for prediction: Danilo Mandic, Jonathon Chambers
Recurrent Neural Networks for Prediction Learning Algorithms, Architectures and Stability, ISBN-13: 9780471495178, ISBN-10: 0471495174
Categories: Machine learning, Neural networks (Computer science). Contributors: Danilo Mandic - Author. Format: Hardcover
Categories: Machine learning, Neural networks (Computer science), Machine learning. Contributors: Danilo Mandic - Author. Format: Hardcover
Recurrent Neural Networks for Prediction Textbook
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
? 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
