MATLAB Implementation of Wavelet Neural Networks

This documentation presents a comprehensive wavelet neural network implementation in MATLAB. The program provides essential functionality for training wavelet neural networks and performing generalization tasks. Wavelet neural networks represent a powerful computational framework that combines wavelet transform properties with neural network adaptability, making them suitable for various complex problem domains. The implementation includes key algorithms for network initialization using wavelet basis functions, backpropagation training with gradient descent optimization, and generalization testing with cross-validation techniques. Key MATLAB functions incorporated in this implementation cover wavelet coefficient calculation, network parameter optimization, and prediction error analysis. This resource offers significant value for researchers and practitioners interested in neural networks and machine learning, providing practical insights into wavelet neural network principles and applications. The code architecture facilitates easy modification and extension, supporting both educational and research objectives to achieve improved performance results in signal processing, pattern recognition, and predictive modeling applications.