A BP Neural Network Example for Predicting Zhejiang Province's GDP

Case Analysis: Predicting Zhejiang Province's GDP with BP Neural Networks

Introduction BP neural networks, as a classic machine learning algorithm, are widely used in time series forecasting. This article demonstrates how to build a predictive model using BP neural networks through the example of forecasting Zhejiang Province's GDP.

Data Preparation A typical dataset should include historical GDP data for Zhejiang Province along with influencing factors such as fixed asset investment, total import and export volume, and household consumption. Data normalization is essential to eliminate dimensional effects and ensure consistent scaling across features.

Model Construction Network Architecture Design: A single hidden layer structure is recommended, with the input layer nodes corresponding to the feature dimension Activation Function Selection: ReLU is suggested for the hidden layer, while a linear activation function should be used for the output layer Training Parameter Settings: Learning rate between 0.01-0.1, iteration count from 500 to 2000 epochs

Training and Optimization Model performance is evaluated using Mean Squared Error (MSE). Common optimization techniques include: Early stopping to prevent overfitting Learning rate decay strategies Adding Dropout layers for regularization

Result Visualization Output should include two curves for comparison: Historical actual GDP values curve Model predicted values curve Model accuracy is assessed by observing the fitting degree between these two curves

Application Value The model can be applied for: Economic trend prediction Policy effect simulation Development planning formulation

Important Considerations GDP forecasting must account for unexpected events; combining with other economic indicators for comprehensive analysis is recommended. In practical applications, alternative network structures like LSTM (Long Short-Term Memory) may be more suitable for time series data due to their inherent sequential processing capabilities.