Three-Layer Backpropagation Neural Network

Backpropagation Neural Network (BPNN) is a multilayer feedforward network based on error backpropagation, whose three-layer architecture (input layer, hidden layer, and output layer) serves as a classic configuration for solving nonlinear problems. When implementing in MATLAB, input/output parameters can be flexibly adjusted through built-in tools or custom code.

Core Logic Analysis Structural Design: The number of input layer nodes is determined by feature dimensions, while the hidden layer node count is typically determined through trial-and-error methods. The output layer corresponds to the number of target categories or regression values. MATLAB Implementation: Data Preprocessing: Input data requires normalization to prevent training instability caused by numerical disparities. Network Initialization: Use the `feedforwardnet` function to create the network, or manually define weight matrices through custom initialization procedures. Training Configuration: Set learning rates and iteration counts (e.g., using the `trainlm` Levenberg-Marquardt algorithm), then initiate training via the `train` function with specified parameters. Scalability: Supports dynamic adjustment of hidden layers (by modifying network objects) and custom activation functions (such as Sigmoid or ReLU) through function handle assignments.

Important Considerations Overfitting issues can be mitigated through Early Stopping techniques or regularization methods integrated into training options. MATLAB's Neural Network Toolbox provides a visual interface (nntool) suitable for rapid prototyping and validation of network architectures.