BP Neural Network for Control System Identification

The application of BP neural networks in control system identification represents a classic nonlinear modeling approach. Its core methodology involves approximating the input-output characteristics of controlled objects through multilayer feedforward networks. From an implementation perspective, key stages include:

First, constructing a standard three-layer network architecture: the input layer handles system input signals, hidden layers perform nonlinear feature extraction, and the output layer generates identification results. Network weights are iteratively updated using backpropagation algorithm, which computes gradients of output errors with respect to weights and adjusts them in the negative gradient direction. In code implementation, this typically involves defining activation functions (like sigmoid/tanh) and implementing gradient descent optimization.

For system identification tasks, the standard workflow involves: collecting response data under typical input signals as training samples; performing data normalization preprocessing; splitting datasets into training/validation sets - the former for weight adjustment, the latter for generalization verification. Code implementation would require data acquisition routines and preprocessing functions like MinMax scaling.

During offline training, critical parameters include learning rate and iteration count: excessive learning rates cause oscillation while insufficient rates slow convergence; inadequate iterations lead to underfitting while excessive iterations risk overfitting. Cross-validation should be implemented programmatically to determine optimal parameters, typically through k-fold validation loops.

The trained network can be deployed for simulation prediction. By comparing network outputs with actual sampling point trajectories, identification accuracy can be visually assessed. Performance metrics commonly include Mean Squared Error (MSE) and R-squared values. When steady-state errors persist, code modifications may involve increasing hidden layer neurons or incorporating momentum factors into the optimization algorithm, such as implementing Nesterov accelerated gradient.