Fundamental Program Code for Adaptive Filtering Algorithms

The following code examples demonstrate fundamental implementations of adaptive filters, designed to enhance understanding and provide practical assistance. Adaptive filters serve as crucial tools in signal processing that automatically adjust filter parameters based on input signal characteristics, thereby improving signal quality and accuracy. The presented code illustrates core functionality including signal input handling, parameter adaptation mechanisms, and output visualization. Key implementations cover gradient-based algorithms like LMS (Least Mean Squares) and RLS (Recursive Least Squares), featuring real-time coefficient updates through error minimization techniques. Each code segment includes comments explaining the adaptation loop structure, weight update equations, and convergence criteria monitoring. These practical examples help developers understand how to initialize filter coefficients, implement learning rate control, and handle signal buffers efficiently for real-time processing applications.