MATLAB Simulation Comparison of RLS and LMS Algorithms in Adaptive Filters

In adaptive filter systems, two commonly used algorithms are the Recursive Least Squares (RLS) algorithm and the Least Mean Square (LMS) algorithm. These algorithms can be compared through MATLAB simulations to evaluate their performance and effectiveness under different conditions. The simulation program allows configuration of various parameters and input signals, enabling observation and analysis of output results. Key implementation aspects include: RLS algorithm implementation using matrix inversion lemma for recursive weight updates with forgetting factor control, while LMS employs gradient descent method with step-size parameter for simpler computation. Through comparative analysis of these algorithms, we can better understand their advantages/disadvantages and practical applicability in real-world scenarios. The MATLAB code typically involves functions like filter() for signal processing, mean-square error calculations for performance evaluation, and iterative weight update loops for adaptive learning processes.