MATLAB Implementation of Digit Recognition Using Backpropagation Neural Network

This MATLAB program implements digit recognition using a backpropagation neural network approach. The implementation encompasses key computational steps: data preprocessing for input normalization, neural network architecture design with configurable hidden layers, weight initialization using methods like Xavier or random initialization, forward propagation with activation functions (e.g., sigmoid or ReLU), error calculation through loss functions like cross-entropy, backpropagation for gradient computation, and weight updating via optimization algorithms such as stochastic gradient descent. The code includes dataset partitioning into training and testing subsets, with real-time monitoring of performance metrics like accuracy, loss convergence, and confusion matrices during training. Through iterative adjustments to network hyperparameters (layer sizes, learning rates, epochs) and architecture tuning, the program enhances digit recognition accuracy and robustness. The modular design allows extension to other classification tasks such as image recognition or speech processing by adapting input dimensions and output classes. Key functions likely include `preprocessData` for input normalization, `initNetwork` for layer initialization, `trainNetwork` implementing the backpropagation loop, and `predict` for inference. The program demonstrates practical neural network implementation with matrix operations optimized for MATLAB's computational environment.