Nonlinear System Modeling Using BP Neural Networks

This example demonstrates nonlinear system modeling through function fitting using BP (Backpropagation) Neural Network theory implemented in MATLAB. The implementation follows a systematic approach: first collecting relevant datasets and performing data preprocessing including normalization and splitting into training/validation sets. The BP neural network architecture is then configured with appropriate hidden layers and activation functions (typically sigmoid or tanh for nonlinear mapping). Key MATLAB functions used include `feedforwardnet` for network creation, `train` for model training with backpropagation algorithm, and `sim` for model simulation. The training process involves iterative weight updates using gradient descent optimization to minimize mean squared error between predicted and actual outputs. After completing the training phase with convergence verification, the model undergoes rigorous validation and performance evaluation through metrics like RMSE and R-squared values. The trained model is ultimately deployed for nonlinear system modeling tasks, demonstrating effective pattern recognition and approximation capabilities for complex nonlinear relationships. This workflow provides a comprehensive methodology for understanding and analyzing nonlinear systems while establishing an effective approach for accurate modeling and function fitting through neural network implementation.