Adaptive Filtering Prediction and Control (Dover Books on Electrical Engineering)
Ideal for advanced undergraduate and graduate classes, this treatment consists of two parts. The first section concerns deterministic systems, covering models, parameter estimation, and adaptive prediction and control. The second part examines stochastic systems, exploring optimal filtering and prediction, parameter estimation, adaptive filtering and prediction, and adaptive control. Extensive appendices offer a summary of relevant background material, making this volume largely self-contained. Readers will find that these theories, formulas, and applications are related to a variety of fields, including biotechnology, aerospace engineering, computer sciences, and electrical engineering.
Why Read This Book
You should read this book if you want a unified, mathematically rigorous treatment of adaptive filtering, prediction and adaptive control for discrete-time systems that ties estimation theory to practical algorithm design. It gives you both deterministic and stochastic viewpoints and shows how classical estimation and control ideas extend into adaptive algorithms used in DSP and control applications.
Who Will Benefit
Advanced undergraduates, graduate students, researchers, and practicing engineers working on adaptive filters, prediction algorithms, system identification, or adaptive control for DSP and control systems.
Level: Advanced — Prerequisites: Undergraduate-level signals and systems, linear algebra, basic probability and stochastic processes, and familiarity with control or state-space concepts; MATLAB or numerical computation experience is helpful.
Key Takeaways
- Derive and analyze parameter estimation methods for linear discrete-time models using deterministic and stochastic frameworks.
- Implement and evaluate adaptive prediction and filtering algorithms (gradient and least-squares based) and understand their convergence properties.
- Apply recursive estimation techniques (e.g., RLS/Kalman-style recursions) for online system identification and tracking.
- Design adaptive control laws based on identified models and assess stability/performance tradeoffs in discrete-time adaptive controllers.
- Extend core linear adaptive methods to practical considerations and basic nonlinear/generalization strategies.
Topics Covered
- 1. Introduction and overview of adaptive methods
- 2. Models for discrete-time systems
- 3. Deterministic parameter estimation
- 4. Adaptive prediction (deterministic viewpoint)
- 5. Adaptive control in deterministic systems
- 6. Stochastic models and optimal filtering
- 7. Prediction for stochastic systems
- 8. Parameter estimation for stochastic systems
- 9. Adaptive filtering algorithms and analysis (gradient, least-squares, recursive methods)
- 10. Adaptive prediction and stochastic adaptive control
- 11. Convergence, stability and performance analysis
- 12. Extensions to nonlinear systems and practical issues
- 13. Numerical and implementation considerations, examples and case studies
Languages, Platforms & Tools
How It Compares
Covers similar ground to Haykin's 'Adaptive Filter Theory' but with stronger emphasis on prediction, system identification and control; complements Sayed's 'Fundamentals of Adaptive Filtering' which is more modern and algorithm-focused.
