Adaptive Noise Filtering Techniques with Code Implementation

This collection contains 7 source code implementations focusing on adaptive noise filtering technology. The code examples cover multiple approaches: wavelet analysis-based filtering, LMS adaptive filters (implementing gradient descent optimization), RLS filters (using recursive algorithms for faster convergence), NLMS filters (with normalized step size for improved stability), feedforward neural networks (featuring single-pass forward propagation), and BP neural network adaptive denoising (utilizing backpropagation for weight updates). These implementations provide diverse methodologies for adaptive noise cancellation, allowing selection of appropriate techniques based on specific application requirements. Each implementation includes key algorithmic components - whether using wavelet thresholding for frequency-domain processing or neural network training for pattern recognition, these code examples offer effective solutions for noise filtering problems with practical implementation considerations.