BP Artificial Neural Network Implementation with Cross-Validation

This is a MATLAB-implemented Backpropagation (BP) artificial neural network program that employs cross-validation for parameter optimization. The program can be applied to solve various problems including image classification, prediction tasks, and pattern recognition. Through systematic adjustment of network parameters (such as learning rate, momentum factor) and layer configurations (number of hidden layers and neurons), users can optimize network performance. The implementation includes functionality for partitioning datasets into distinct training and testing sets, enabling rigorous validation and evaluation of network generalization capabilities. Key implementation features include: - Cross-validation module that automates parameter tuning through k-fold validation techniques - Configurable neural network architecture supporting customizable hidden layers and activation functions - Integrated performance metrics calculation (accuracy, precision, recall) for objective evaluation - Batch and stochastic training modes with convergence monitoring This program serves as a powerful and flexible tool for both understanding ANN working mechanisms and solving practical engineering problems. The code structure allows easy modification of network parameters and experimental setups, making it suitable for research and educational purposes.