- 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 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.
Network Architectures for Prediction.
Activation Functions Used in Neural Networks.
Recurrent Neural Networks Architectures.
Neural Networks as Nonlinear Adaptive Filters.
Stability Issues in RNN Architectures.
Data–Reusing Adaptive Learning Algorithms.
A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks.
Convergence of Online Learning Algorithms in Neural Networks.
Some Practical Considerations of Predictability and Learning Algorithms for Various Signals.
Exploiting Inherent Relationships Between Parameters in Recurrent Neural Networks.
Appendix A: The O Notation and Vector and Matrix Differentiation.
Appendix B: Concepts from the Approximation Theory.
Appendix C: Complex Sigmoid Activation Functions, Holomorphic Mappings and Modular Groups.
Appendix D: Learning Algorithms for RNNs.
Appendix E: Terminology Used in the Field of Neural Networks.
Appendix F: On the A Posteriori Approach in Science and Engineering.
Appendix G: Contraction Mapping Theorems.
Appendix H: Linear GAS Relaxation.
Appendix I: The Main Notions in Stability Theory.
Appendix J: Deasonsonalising Time Series.