FWNN Implementation Source Code for Fuzzy Wavelet Neural Network

This documentation provides the complete implementation source code for FWNN (Fuzzy Wavelet Neural Network). The code demonstrates how to train a fuzzy wavelet neural network using the Gradient Descent (GD) optimization algorithm. The implementation includes wavelet basis functions for feature transformation and fuzzy logic components for handling uncertain information. Key functions cover network initialization, forward propagation calculations, and parameter updates through gradient computation. The GD algorithm iteratively adjusts connection weights and membership function parameters to minimize the loss function. Through studying this source code, you'll gain practical understanding of FWNN architecture, wavelet decomposition techniques, and gradient-based optimization methods. The code structure includes modular components for data preprocessing, network configuration, training loops, and performance evaluation. This implementation serves as a valuable resource for both learning and research applications in hybrid intelligent systems.