BP Neural Network MATLAB Algorithm Implementation

In this documentation, I would like to share my self-developed MATLAB implementation of the Backpropagation (BP) Neural Network algorithm. This implementation follows the fundamental principles and theoretical framework of BP neural networks, utilizing MATLAB M-files for practical execution. During the development process, I extensively referenced relevant literature and technical resources, conducting numerous experiments and parameter adjustments to ensure algorithm accuracy and effectiveness. The implementation includes key components such as: - Network initialization with configurable layers and neurons - Forward propagation calculations with activation functions - Backpropagation error computation and weight updates - Training iterations with convergence criteria Through this algorithm, various types of data can be processed for classification and prediction tasks, providing practical solutions for real-world problems. The code structure emphasizes modular design, allowing users to easily modify network architecture and training parameters according to specific application requirements. I hope this documentation serves as a platform to share my programming methodology and development insights, and that this implementation proves beneficial for other researchers and developers working with neural networks. The algorithm demonstrates proper handling of gradient descent optimization and includes features for performance monitoring during training cycles.