MATLAB Implementation of Backpropagation Neural Network for Binary Classification

This is a high-quality implementation of a Backpropagation (BP) neural network designed for effective binary classification and recognition of two-class data. The program utilizes an advanced algorithm based on gradient descent optimization with error backpropagation, enabling accurate analysis and processing of input data while performing classification based on data characteristics. Key implementation features include: - Forward propagation computation using sigmoid activation functions - Error calculation through mean squared error (MSE) loss function - Weight updates via backpropagation with adjustable learning rate - Network architecture configuration with customizable hidden layers The program demonstrates excellent scalability and stability, capable of handling large datasets while maintaining high performance efficiency. With applications spanning both scientific research and practical implementations, this solution offers accurate and reliable classification results through its robust neural network architecture. The code includes comprehensive parameter tuning options for optimal performance across various dataset types.