BP Neural Network MATLAB Implementation

This MATLAB source code implements a Backpropagation (BP) neural network with comprehensive simulation capabilities. The program enables complete neural network training and testing operations through configurable parameters. Key implementation features include data preprocessing and feature extraction modules that enhance neural network performance by normalizing input data and selecting relevant features. The code structure allows users to adjust critical network parameters such as learning rate, number of hidden layers, and activation functions (typically implemented using sigmoid or tanh functions). Through systematic parameter optimization, users can improve the network's learning capacity and generalization ability. The algorithm implements the standard backpropagation method with gradient descent optimization, including forward propagation for prediction and backward propagation for weight updates. This source code serves as a valuable tool for researchers and developers conducting experiments and studies in neural network applications, providing a flexible framework for pattern recognition, prediction tasks, and classification problems.