Wavelet Neural Network Prediction Example Source Code

This repository provides a practical source code example for prediction using wavelet neural networks. The implementation showcases key components including data normalization/denormalization routines, wavelet basis function selection, and backpropagation training algorithms. The code architecture allows flexible adaptation to various prediction tasks by modifying network parameters like hidden layer neurons, wavelet types, and learning rates. The wavelet neural network model effectively transforms input data into wavelet coefficients through hidden layers, leveraging wavelet decomposition's multi-resolution analysis for enhanced prediction accuracy. The complete workflow covers data preprocessing techniques, network initialization methods, iterative training processes with convergence monitoring, and final prediction modules. This example serves as an educational foundation for understanding wavelet neural network implementation strategies, with modular code structure enabling easy customization for specific applications. The commented source code provides insights into gradient calculation, weight updating mechanisms, and wavelet activation function implementations to support academic research and industrial applications.