MATLAB Implementation of BP Neural Network for Prediction

This repository provides a MATLAB-implemented BP (Backpropagation) neural network prediction program. The code implements a multilayer perceptron with sigmoid activation functions and gradient descent optimization, capable of handling various datasets for time-series forecasting, regression analysis, and pattern recognition. Key features include configurable hidden layers, adjustable learning rates, and momentum coefficients to prevent local minima. The program incorporates data normalization preprocessing and performance evaluation metrics (e.g., MSE, R-squared) for result interpretation. Practical applications have demonstrated its reliability in financial forecasting, energy consumption prediction, and industrial process modeling. Detailed documentation covers data formatting requirements, parameter tuning guidelines, and output analysis procedures. For optimal performance, users can modify network topology through the 'net.layers' property and monitor training progress via the built-in convergence plots. Download includes example datasets and script templates for rapid implementation. Technical support and customization services are available upon request.