MATLAB Implementation of Backpropagation Neural Network

This is an original MATLAB implementation of a Backpropagation (BP) Neural Network. The code provides a complete framework for implementing the fundamental BP network algorithm, supporting both neural network training and prediction operations. The BP neural network represents a widely-used artificial neural network model that employs backpropagation algorithms to adjust network weights and biases, enabling effective fitting and prediction of input data patterns.

The implementation includes critical computational stages: network initialization with customizable layer configurations, forward propagation using activation functions (typically sigmoid or tanh), error calculation through mean squared error or cross-entropy methods, and backward propagation for gradient-based weight updates. Key functions handle parameter initialization, matrix operations for efficient computation, and iterative training loops with convergence checks.

Users can modify and extend this foundational codebase to address diverse problems and datasets by adjusting network architecture (hidden layers, neurons), learning parameters (learning rate, momentum), or activation functions. The modular structure allows for easy integration of additional features like regularization techniques, batch processing, or advanced optimization algorithms.