MATLAB Implementation of Backpropagation Neural Network Algorithm

This MATLAB implementation of the Backpropagation Neural Network algorithm provides a complete solution for predictive modeling. The program loads a pre-trained neural network from the ANN.mat file and applies it to new datasets for prediction tasks. The implementation includes several key components: data loading and preprocessing functions that handle input normalization, network loading using MATLAB's load() function to retrieve the trained weights and biases, and a prediction module that implements the forward propagation algorithm through matrix operations and activation functions. The code calculates mean squared error (MSE) using MATLAB's built-in functions to quantitatively evaluate prediction accuracy. Additionally, it generates comparative visualization plots using MATLAB's plotting functions (plot(), legend(), title()) to display side-by-side comparisons between predicted results and original data, helping users visually assess model performance. This comprehensive implementation demonstrates practical application of BP neural networks in MATLAB, featuring error metric calculation and result visualization for effective data analysis and model validation. The code structure follows MATLAB best practices for neural network implementation, making it suitable for both educational and research purposes.