Implementation of BP Neural Network for Nonlinear Function Approximation

This program implements Backpropagation (BP) Neural Network using MATLAB to approximate nonlinear functions. The implementation employs a multi-layer perceptron architecture trained with the backpropagation algorithm, which adjusts network weights through gradient descent optimization to minimize the error between predicted and target outputs. The model includes configurable parameters such as hidden layer size, learning rate, and activation functions (typically sigmoid or tanh) to handle complex nonlinear mappings. The package contains MATLAB m-files implementing the core BP algorithm, validation scripts demonstrating function approximation performance, and detailed documentation. These files comprehensively document the implementation workflow, parameter tuning strategies, and usage instructions, enabling users to understand and utilize the program effectively. Key functions include network initialization, forward propagation, error calculation, backward weight updates, and convergence checking. This implementation allows users to rapidly and accurately approximate nonlinear functions, providing a robust foundation for further research and practical applications in pattern recognition, system modeling, and predictive analysis. The modular code structure facilitates customization of network architecture and training parameters for specific function approximation tasks.