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Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability |
| Danilo Mandic (University of East Anglia, UK); Jonathon Chambers (University of Bath, UK) |
| 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. Ã Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting
à Examines stability and relaxation within RNNs | | |
