MATLAB Code Implementation for Neural Network Applications

This implementation applies MATLAB to neural networks, specifically utilizing the Backpropagation (BP) algorithm and Sigmoid activation function to study function approximation problems. The research involves constructing neural network architectures with varying hidden layers and nodes, implementing gradient descent optimization through BP algorithm for weight updates, and applying Sigmoid transfer functions for non-linear transformations. Key MATLAB functions like feedforwardnet for network creation and train for supervised learning can be employed. Researchers can explore different network topologies, adjust learning rates and training epochs, and test approximation capabilities on diverse mathematical functions. This approach provides insights into neural network mechanics while delivering accurate approximation results for practical applications. Furthermore, MATLAB's integration with Deep Learning Toolbox and external libraries enables extended research scope through advanced optimization techniques and custom activation functions.