MATLAB Implementation of Backpropagation Neural Network Algorithm

Backpropagation (BP) Neural Network is a classic multilayer feedforward neural network that adjusts weights through error backpropagation algorithm, making it highly suitable for solving pattern recognition problems. In transformer fault diagnosis, BP networks can effectively learn nonlinear mapping relationships between fault characteristics and fault types.

The implementation process primarily consists of the following steps: Data Preparation Phase Using transformer simulation data to construct training and test sets. Original data typically requires normalization to eliminate dimensional effects. The dataset should include various fault characteristic parameters (such as dissolved gas content in oil, temperature, etc.) and corresponding fault type labels.

Network Structure Design The number of input layer nodes corresponds to the dimension of fault characteristics, while the output layer node count matches the number of fault categories. A single hidden layer structure is commonly used, with the number of nodes determined through empirical formulas or trial-and-error methods. MATLAB's `newff` function can quickly initialize the network architecture with specified layer sizes and transfer functions.

Key Parameter Settings Appropriate selection of learning rate (controlling weight update magnitude), training epochs (preventing under/overfitting), and error targets is essential. Activation functions typically use Sigmoid for hidden layers and Linear or Softmax functions for output layers. The `trainlm` (Levenberg-Marquardt) or `traingd` (gradient descent) functions can be specified as training algorithms.

Training and Testing The `train` function is called for network training, where MATLAB automatically performs forward propagation and error backpropagation iterations. During training, the mean squared error curve can be monitored to judge convergence. In the testing phase, the `sim` function predicts new samples, and classification accuracy is evaluated through confusion matrices using MATLAB's `confusionmat` function.

Practical considerations: Data quality determines the model's upper limit, and dimensionality reduction techniques like Principal Component Analysis (PCA) can be integrated. Overfitting issues can be mitigated through regularization or early stopping methods. For complex fault patterns, combining other intelligent algorithms like Genetic Algorithms can optimize network performance through hybrid approaches.