Data Classification Using BP Neural Network with MATLAB Implementation

The implementation of data classification using BP neural network based on MATLAB involves creating a multi-layer feedforward network trained with error backpropagation algorithm. This approach requires defining network architecture parameters such as the number of hidden layers, neurons per layer, activation functions (typically sigmoid or tanh), and training parameters including learning rate and momentum factor. In MATLAB implementation, key functions include `feedforwardnet` for network creation, `train` for model training using algorithms like Levenberg-Marquardt or gradient descent, and `sim` for classification prediction. The training process involves forward propagation of input data, error calculation at the output layer, and backward propagation of errors to adjust synaptic weights through partial derivative calculations. BP neural network demonstrates strong pattern recognition capabilities through iterative weight optimization, enabling accurate categorization of different data classes. This method provides effective data classification and identification support, forming a robust foundation for subsequent data analysis and practical applications. The implementation typically includes data preprocessing, network configuration, training cycle management, and performance evaluation using confusion matrices or ROC curves.