BP Neural Network for Function Fitting and Pattern Recognition

This example demonstrates MATLAB implementation of Backpropagation (BP) neural networks for both function fitting and pattern recognition applications. BP neural networks represent a widely used type of artificial neural network capable of learning and approximating complex nonlinear relationships through supervised training. The implementation showcases practical applications of BP networks using MATLAB's neural network toolbox, featuring key functions such as feedforward computation, error backpropagation, and gradient-based weight updates. The code structure includes data preprocessing, network architecture configuration (including hidden layer sizing and activation function selection), training parameter optimization, and performance validation through metrics like mean squared error for regression tasks and classification accuracy for pattern recognition. Through this practical implementation, users will gain comprehensive understanding of BP network principles and learn to apply them effectively to solve real-world function approximation and pattern classification challenges.