Wavelet Neural Network Source Code Implementation

This repository contains essential source code implementations for wavelet neural networks, offering significant assistance in studying this advanced computational architecture. The codebase includes fundamental theoretical frameworks and practical wavelet transform algorithms integrated with neural network components. Key implementations feature multi-resolution analysis capabilities through discrete wavelet transforms, activation functions based on wavelet scaling, and hybrid training methods combining backpropagation with wavelet coefficient optimization. Through detailed code examination, researchers can understand critical aspects like wavelet node construction, feature extraction mechanisms using wavelet packets, and time-frequency analysis integration. The practical examples demonstrate real-world applications including signal denoising, pattern recognition, and non-stationary data processing, providing a solid foundation for advanced research and industrial applications of wavelet neural networks.