BP Neural Network Prediction

BP neural network is a classic artificial neural network model widely applied to various prediction problems such as traffic flow forecasting and stock price prediction. Its core principle involves continuously adjusting network weights through backpropagation algorithm to minimize the difference between predicted outputs and actual values.

In traffic flow prediction scenarios, BP neural networks establish nonlinear mapping relationships between inputs and outputs by learning temporal patterns from historical traffic data. A typical network architecture consists of an input layer (receiving historical flow data), hidden layers (processing feature mapping), and an output layer (predicting future period flows).

MATLAB implementation typically involves these key steps: Data Preprocessing - Normalize raw traffic flow data to eliminate dimensional effects and improve network convergence speed using functions like mapminmax or zscore Network Initialization - Configure hyperparameters including number of hidden layer neurons, learning rate, and activation functions (e.g., sigmoid or ReLU) through net.trainParam settings Forward Propagation - Compute layer outputs sequentially using matrix multiplication and activation functions until obtaining prediction results Error Backpropagation - Adjust weight parameters layer-by-layer based on prediction errors through gradient descent optimization Iterative Training - Repeat forward/backward propagation cycles using train function until meeting preset accuracy thresholds or maximum epochs

The method's advantage lies in its capability to automatically learn complex nonlinear relationships, though attention must be paid to overfitting issues and parameter tuning techniques. Practical applications often incorporate cross-validation methods to determine optimal network structures.