BP Neural Network for Nonlinear System Modeling - Nonlinear Function Fitting

BP (Backpropagation) Neural Network is a widely used method for nonlinear system modeling. It achieves system modeling through nonlinear function fitting, making it a classic approach in this field. The network typically implements this through multilayer perceptron architecture with sigmoid or tanh activation functions, using gradient descent optimization to minimize error between predicted and actual outputs. Additionally, BP neural networks can be applied to pattern recognition, data classification, and other machine learning tasks, often implemented using matrix operations for efficient forward propagation and error backpropagation. When using BP neural networks for system modeling, key implementation steps include network structure design (determining hidden layers and neurons), parameter selection (learning rate, momentum factor), and training iteration control, all of which contribute to better model fitting performance. Common implementations involve initializing weights randomly, calculating output through forward propagation, computing error gradients via backpropagation, and updating weights using optimization algorithms. Overall, BP neural networks serve as highly practical tools with broad application prospects in both scientific research and engineering applications, particularly in MATLAB environments where functions like 'feedforwardnet' and 'train' simplify the implementation process.