MATLAB Implementation of Backpropagation Neural Network Algorithm

The Backpropagation (BP) neural network algorithm is a pattern recognition algorithm used for identifying equipment fault types. In this example, we implement it for transformer fault identification using transformer simulation data. The MATLAB implementation typically involves creating a multi-layer perceptron network with sigmoid activation functions, where the training process utilizes gradient descent optimization to minimize the mean squared error between predicted and actual fault classifications. Key implementation steps include preprocessing the simulation data through normalization, designing the network architecture with appropriate hidden layers, and training the model using the backpropagation algorithm with momentum for stable convergence. The trained network can effectively identify and classify transformer fault types with high accuracy, helping detect potential transformer failures early and enabling timely maintenance actions to ensure normal equipment operation and extend service life. This algorithm demonstrates significant practical value in equipment fault type recognition, with MATLAB providing built-in functions like 'feedforwardnet' and 'train' for streamlined implementation. The code typically handles data partitioning into training/validation sets, implements early stopping to prevent overfitting, and includes performance evaluation metrics such as confusion matrices and classification accuracy calculations.