A Data Processing Source Code Implementation Based on Backpropagation Neural Network

Backpropagation (BP) neural network is a widely-used machine learning model extensively applied in data classification, regression analysis, and pattern recognition. This article provides a comprehensive explanation of the data processing pipeline and implementation methodology based on BP neural networks.

The core mechanism of BP neural networks lies in the backpropagation algorithm, which iteratively adjusts network weights and biases to minimize prediction errors. Data processing forms the foundation of neural network training, involving crucial steps such as data preprocessing, feature extraction, and normalization.

During the initial data preprocessing phase, raw data undergoes cleaning and standardization to ensure data quality. Common preprocessing techniques include missing value imputation (using methods like mean/median filling), outlier handling (through IQR or Z-score methods), and data normalization. Normalization accelerates model convergence, typically implemented using Min-Max scaling or Z-Score standardization in code.

The subsequent network architecture construction involves designing input, hidden, and output layers. The input layer node count corresponds to feature dimensions, while output layer configuration depends on task requirements (e.g., number of classes for classification tasks). Hidden layer configuration (layer count and node numbers) requires empirical tuning or cross-validation optimization.

During training, the model computes predictions through forward propagation and updates weights via error backpropagation. Common loss functions include Mean Squared Error (MSE) for regression and Cross-Entropy Loss for classification tasks. Optimization algorithms like Stochastic Gradient Descent (SGD) or Adam are implemented for parameter adjustment using gradient computation.

Model performance evaluation utilizes test datasets with metrics such as accuracy, precision, and recall. Overfitting mitigation techniques include L2 regularization (weight decay implementation) and Dropout methods (random neuron deactivation during training).

BP neural network training represents an iterative optimization process where systematic data processing and parameter tuning significantly enhance model performance through controlled epochs and learning rate adjustments.