Adaptive Algorithm Employs NLMS Algorithm with Enhanced Code Implementation

1. The adaptive algorithm adopts the NLMS (Normalized Least Mean Square) algorithm from reference [2], providing faster convergence speed and reduced training iterations. Implementation typically involves calculating step size normalization using input signal power estimation. 2. Adaptive convergence step size has optimal values within (0, 2) range, with Lorenz sequence analysis determining 0.6 as the optimal parameter through empirical validation.

Iris Data Classification Using Backpropagation Neural Network

Implementation of BP neural network for iris data classification, where achieving over 99% classification accuracy is possible by appropriately adjusting training epochs and precision parameters through code optimization.