Application of BP Neural Network in Population Prediction

BP neural network is a widely used machine learning algorithm that plays a significant role in population prediction and other domains. By analyzing population data from previous years, it establishes mathematical models through forward propagation and error backpropagation mechanisms to forecast future population numbers. This method employs gradient descent optimization to minimize prediction errors by adjusting network weights and biases iteratively. The implementation typically involves using activation functions like sigmoid or ReLU in hidden layers, with mean squared error as the loss function for training. This approach assists governments and researchers in making informed decisions and formulating effective policies to address challenges arising from demographic changes. Furthermore, BP neural networks can be applied to other fields such as stock market prediction and weather forecasting, providing enhanced reference capabilities and predictive power through similar training methodologies involving data normalization, epoch iteration, and validation set evaluation.