The Complete Training Process of BP Neural Networks

The complete training process of Backpropagation (BP) neural networks is crucial and involves multiple sequential steps. Initially, data filtering is performed to remove outliers and ensure training accuracy and reliability, typically implemented through statistical methods like z-score analysis or IQR techniques. Following this, data smoothing is applied using algorithms such as moving averages or Savitzky-Golay filters to eliminate noise and anomalous values that could disrupt learning. Subsequently, data normalization is executed using techniques like min-max scaling or z-score standardization to ensure comparability between different features and improve convergence speed during training.

The core of the process involves constructing the training network architecture, which includes determining the number of hidden layers, neurons per layer, and activation functions (commonly sigmoid or ReLU). The network is then trained using gradient descent optimization with backpropagation algorithm to minimize the loss function. After training completion, denormalization is performed to convert the normalized outputs back to their original scale using inverse transformation functions.

Finally, visualization techniques are employed to plot fitting results, comparing predicted outputs against actual values using scatter plots or regression charts to evaluate training effectiveness. Performance metrics like RMSE and R-squared are calculated to quantify accuracy. In conclusion, the BP neural network training process represents a sophisticated and critical workflow requiring systematic data preprocessing, network configuration, and post-processing validation to ensure practical applicability and reliability in real-world scenarios.