Source Code for a Typical Wavelet Neural Network Implementation

This document presents the complete source code implementation for a typical wavelet neural network (wavelet network). The codebase not only holds substantial academic value but also serves as an excellent reference for studying wavelet neural network architectures. The implementation includes core components such as wavelet activation functions, network initialization methods, and backpropagation algorithms with detailed comments explaining parameter tuning and gradient calculations. Through comprehensive code demonstrations, readers can gain insights into the working principles and algorithmic details of wavelet neural networks, including weight update mechanisms and wavelet coefficient optimization. Additionally, I provide practical examples and application scenarios demonstrating how to apply this wavelet network to signal processing and pattern recognition tasks. The code structure follows modular design principles with separate functions for forward propagation, error computation, and wavelet transformation layers. Overall, this documentation serves as a valuable and thorough resource for researchers and practitioners interested in wavelet neural networks, featuring production-ready code with configuration parameters for different dataset scales.