Three-Layer BP Artificial Neural Network Data Training Program with Code Implementation Guide

This document describes a three-layer BP (Backpropagation) artificial neural network data training program. The implementation involves multiple code modules serving distinct functionalities. The neural network architecture is constructed using three primary layers: an input layer for data reception, a hidden layer for feature processing, and an output layer for prediction generation. The core training mechanism employs the backpropagation algorithm, which calculates error gradients layer-by-layer to adjust network parameters through iterative optimization. Key implementation aspects include weight initialization methods (e.g., Xavier initialization), activation function selection (typically sigmoid or ReLU), and learning rate configuration for gradient descent updates. Additional features integrated into the program comprise model serialization/deserialization functions for saving and loading trained networks, alongside prediction interfaces for processing new datasets. The codebase demonstrates practical neural network implementation techniques, making it equally valuable for beginners learning fundamental ANN concepts and experienced developers seeking modular BP network implementations. The program includes error monitoring modules with convergence tracking and validation accuracy metrics to assess training effectiveness.