BP Neural Network Example for Zhejiang Province GDP Prediction

In this example, we implement a BP (Backpropagation) neural network to predict GDP trends in Zhejiang Province. The neural network model is constructed using well-structured data processing code that efficiently handles input charts and datasets for comprehensive analysis. The implementation demonstrates key components including data normalization using Min-Max scaling, network architecture design with input-hidden-output layers, and the backpropagation algorithm for weight optimization through gradient descent. This approach enables accurate economic trend forecasting, supporting better understanding of Zhejiang's economic development patterns and facilitating data-driven decision making. The example further highlights neural networks' potential in economic forecasting applications, providing valuable research directions for extending this methodology to other regional economies. Through this practical implementation, users can gain deeper insights into BP neural network工作机制 (working mechanisms), including activation functions (typically sigmoid/tanh for hidden layers), error calculation via Mean Squared Error, and iterative training processes. This serves as a valuable reference for research projects involving time-series prediction and pattern recognition applications.