Adaptive Filtering in MATLAB

This original MATLAB program provides detailed implementations of adaptive filtering techniques, featuring complete LMS (Least Mean Squares) and RLS (Recursive Least Squares) algorithms. The code demonstrates practical signal processing applications where adaptive filters dynamically adjust their coefficients to optimize system performance. Key implementation aspects include: step-size parameter tuning in LMS for convergence control, forgetting factor implementation in RLS for tracking non-stationary signals, and real-time coefficient updating mechanisms. The program serves as an educational resource for understanding adaptive filtering principles through executable examples with synthetic and real-world signal data. Additional resources include theoretical background notes and sample datasets showing applications in system identification, noise cancellation, and channel equalization. Users can modify algorithm parameters, test different filter structures, and extend the code for specific signal processing requirements. This implementation provides a foundation for further exploration of advanced adaptive filtering techniques and their practical applications in digital signal processing.