Five-Input Three-Output Standard Backpropagation Neural Network

This project implements a standard backpropagation (BP) neural network with five inputs and three outputs using pure MATLAB programming, completely avoiding the Neural Network Toolbox. The implementation will involve manual coding of all neural network components including forward propagation, error calculation, and weight updates through backpropagation. We will begin by understanding the fundamental principles of standard BP neural networks, including the mathematical foundation of gradient descent optimization and the chain rule for error backpropagation. The implementation will include creating custom MATLAB functions for initializing network weights randomly, computing sigmoid activation functions for hidden and output layers, and implementing the backpropagation algorithm for weight adjustments. The code architecture will feature: - Input layer with 5 nodes corresponding to input features - Hidden layer with adjustable number of neurons (configurable parameter) - Output layer with 3 nodes for prediction results - Manual implementation of forward propagation using matrix multiplication and activation functions - Calculation of mean squared error between predicted and actual outputs - Backward propagation of errors with weight updates using learning rate parameter - Iterative training process with epochs and convergence criteria By implementing this entirely in base MATLAB, we gain deeper insight into neural network mechanics, including weight initialization strategies, learning rate optimization, and convergence monitoring. This approach provides complete customization capabilities for network architecture adjustments, activation function modifications, and optimization algorithm enhancements. The project serves as an excellent educational tool for understanding neural network fundamentals while demonstrating how to build ML algorithms from scratch. This hands-on implementation will strengthen understanding of gradient-based optimization and prepare for more complex neural network architectures in future projects.