Explore MATLAB source code curated for "遗忘因子" with clean implementations, documentation, and examples.
An improved LMS algorithm and its application in noise cancellation. Building upon traditional fixed-step and variable-step LMS algorithms, this enhanced variable-step method utilizes exponential cubic instantaneous error magnitude with a forgetting factor for simultaneous step-size adjustment, effectively balancing convergence speed and steady-state error trade-offs.
Performance comparison between LMS and RLS adaptive filtering algorithms, including weight convergence analysis, effects of forgetting factors, and MATLAB implementation considerations
A practical implementation case of the least squares algorithm with forgetting factor, including simulation models and M-files with detailed, easy-to-understand comments that demonstrate proper code structure and parameter configuration.
Implementation of LMS (Least Mean Squares) and RLS (Recursive Least Squares) algorithms using MATLAB simulation software, including comparative analysis of convergence speeds, post-convergence bit error rate evaluation, and examination of step size impact on LMS algorithm's mean square error performance curves and forgetting factor influence on RLS algorithm performance characteristics. Code implementation covers adaptive filter structures, weight update mechanisms, and real-time performance monitoring.
Comprehensive analysis of LMS (Least Mean Square) and RLS (Recursive Least Squares) algorithms focusing on convergence speed, computational complexity, and dynamic environment adaptability with code implementation insights