XOR Problem Classification Using BP Neural Network

The MATLAB-implemented XOR classification algorithm using Backpropagation (BP) Neural Network effectively solves the XOR problem. BP neural network is a widely-used artificial neural network model that learns mapping relationships between inputs and outputs through training processes. In this implementation, the algorithm utilizes a multi-layer perceptron structure with sigmoid activation functions to handle non-linear classification. The network architecture typically consists of an input layer with two neurons (for XOR inputs), a hidden layer with multiple neurons to capture non-linear patterns, and an output layer with one neuron for classification results. Key implementation aspects include: forward propagation to calculate outputs, error computation using mean squared error, and backward propagation to adjust weights through gradient descent optimization. The training process involves iterative weight updates using derivatives of the activation function to minimize classification error. This algorithm successfully classifies XOR patterns into distinct categories by learning the non-linear decision boundaries. The implementation establishes a foundation for solving more complex pattern recognition problems and demonstrates broad application prospects in areas like logic circuit design, data classification, and non-linear system modeling. The code includes functions for network initialization, training iteration, and performance validation, providing a complete framework for binary classification tasks.