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. 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 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. VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! http://www.wiley.co.uk/commstech/ VISIT OUR WEB PAGE! http://www.wiley.co.uk/
Why Read This Book
You should read this book if you want a focused, DSP-oriented treatment of recurrent neural networks for time-series prediction: it connects RNN architectures and learning rules to classical adaptive-filter thinking and gives analysis of stability and on-line algorithms. You will get both theoretical tools and practical insights for applying RNNs to real-time signal-processing problems.
Who Will Benefit
Graduate students, researchers, and DSP engineers working on time-series prediction, adaptive filtering, or nonlinear signal-processing who need depth on RNN design, stability and on-line learning.
Level: Advanced — Prerequisites: Familiarity with linear systems and DSP fundamentals, linear algebra, calculus, probability/statistics, basic neural-network concepts and adaptive filtering theory.
Key Takeaways
- Design recurrent neural network architectures suitable for time-series prediction and spatio-temporal modelling.
- Analyze stability and relaxation properties of RNNs to ensure reliable real‑time operation.
- Implement online learning algorithms for RNNs (e.g., variants of real-time recurrent learning and training strategies) for adaptive prediction.
- Treat RNNs as nonlinear adaptive filters and relate their behavior to classical adaptive-filter theory.
- Apply RNN-based predictors to practical DSP tasks such as speech/biomedical signal prediction and other time-series forecasting problems.
Topics Covered
- Introduction: Prediction problems and motivation for RNNs
- RNNs as Nonlinear Adaptive Filters — conceptual framework
- Architectures: fully recurrent, spatio-temporal, modular and nested designs
- Learning Rules for RNNs: real‑time recurrent learning and alternatives
- On-line Training: algorithms, complexity and practical considerations
- Stability and Relaxation Analysis of RNNs
- Regularization, Generalization and Convergence Issues
- Implementation Issues and Real‑time Constraints
- Applications to Signal Processing: speech, biomedical and communications examples
- Case Studies and Experimental Results
- Extensions: modularity, nesting and hybrid architectures
- Conclusions and future directions; appendices with mathematical background
Languages, Platforms & Tools
How It Compares
More specialized than Haykin's broad Neural Networks text — Mandic focuses specifically on RNNs for prediction and stability, and complements adaptive‑filter texts (e.g., Haykin's Adaptive Filter Theory) by treating neural nets as nonlinear adaptive filters.
