Principles of Adaptive Filters and Self-learning Systems (Advanced Textbooks in Control and Signal Processing)
Teaches students about classical and nonclassical adaptive systems within one pair of covers
Helps tutors with time-saving course plans, ready-made practical assignments and examination guidance
The recently developed "practical sub-space adaptive filter" allows the reader to combine any set of classical and/or non-classical adaptive systems to form a powerful technology for solving complex nonlinear problems
Why Read This Book
You will get a compact, course-ready treatment of adaptive filtering that bridges classical LMS/RLS theory and more recent subspace and nonclassical methods, with practical assignments and implementation guidance. The book emphasizes techniques you can apply to system identification, noise cancellation, equalization and other real-world adaptive DSP problems.
Who Will Benefit
Graduate students and practicing engineers in communications, audio/speech, radar or control who need to design or implement adaptive filters and self-learning systems for identification, cancellation, or equalization tasks.
Level: Intermediate — Prerequisites: Signals and systems, linear algebra (matrix methods), basic probability/statistics and familiarity with discrete-time DSP; MATLAB experience is helpful.
Key Takeaways
- Implement LMS-family algorithms (LMS, NLMS) and understand their convergence and steady-state behavior.
- Apply RLS and fast adaptive algorithms for rapid tracking and higher performance scenarios.
- Combine classical and nonclassical methods using subspace/adaptive-subspace techniques to tackle complex or nonlinear problems.
- Analyze stability and performance trade-offs of adaptive filters in practical settings.
- Design and test adaptive solutions for system identification, echo/noise cancellation, and adaptive equalization using supplied laboratory-style exercises.
Topics Covered
- 1. Introduction to Adaptive and Self-learning Systems
- 2. Review of Linear Systems and Estimation Principles
- 3. Performance Measures and Mean-square Analysis
- 4. The LMS Family: Algorithms and Practical Variants
- 5. Recursive Least Squares and Fast Algorithms
- 6. Nonclassical Adaptive Schemes and Nonlinear Extensions
- 7. Subspace and Practical Subspace Adaptive Filters
- 8. Combining Classical and Nonclassical Methods
- 9. Applications: Identification, Cancellation, Equalization
- 10. Implementation Issues and Numerical Considerations
- 11. Laboratory Exercises, Assignments and Examination Guidance
- 12. Appendices: Mathematical Background and Simulation Notes
Languages, Platforms & Tools
How It Compares
Covers similar core material to Haykin's Adaptive Filter Theory and Sayed's Fundamentals of Adaptive Filtering but places more practical, course-oriented emphasis on subspace/adaptive-subspace combinations and lab assignments than those more mathematically comprehensive references.
