Adaptive Algorithm Employs NLMS Algorithm with Enhanced Code Implementation

The paper specifies that the adaptive algorithm utilizes the NLMS algorithm, which is thoroughly detailed in reference [2]. Compared to previously employed algorithms, NLMS demonstrates superior convergence speed and requires fewer training iterations. In code implementation, this is achieved through normalized step size adjustment based on instantaneous input power calculation. Regarding the adaptive convergence step size selection, our research identifies that optimal values within the (0, 2) interval significantly enhance algorithm performance. Through experimental verification using Lorenz sequences for computation, we determined 0.6 to be the optimum value. This parameter optimization can be implemented in code using dynamic step size adjustment functions that monitor convergence behavior. The optimization effectively improves algorithm precision and robustness, with the NLMS implementation featuring automatic step size normalization to maintain stability across varying signal conditions.