Fundamental Source Code of Wavelet Neural Networks for Classification

The following presents fundamental source code for wavelet neural networks (WNNs) applied to classification problems. This implementation features carefully designed architecture and optimization techniques, demonstrating how wavelet transforms integrate with neural networks for feature extraction and pattern recognition. The code includes comprehensive inline documentation explaining key components such as wavelet function selection, network initialization, backpropagation algorithms adapted for wavelet neurons, and classification decision mechanisms. Example datasets with corresponding prediction results illustrate practical applications, showcasing the model's ability to handle nonlinear classification boundaries through multi-resolution analysis. Both beginners and experienced developers can utilize these well-structured implementations to understand WNNs' mathematical foundations and adapt them for specific projects. The code employs matrix operations for efficient wavelet decomposition and includes gradient calculation methods tailored for wavelet activation functions. We encourage users to modify parameters like wavelet types (e.g., Morlet, Mexican Hat) and network layers to optimize performance for different data characteristics.