RBF Neural Network Identification Simulation

Analysis of RBF Neural Network Identification Simulation

RBF (Radial Basis Function) neural networks are frequently employed in nonlinear system identification due to their strong local approximation capability and fast convergence speed. Implementing this simulation in a graduation thesis typically involves the following core components:

Network Structure Design A three-layer feedforward structure is adopted: the input layer receives system signals, the hidden layer performs nonlinear transformation through radial basis functions such as Gaussian functions, and the output layer carries out linear weighting. Key parameters include the number of hidden layer nodes (affecting approximation accuracy) and the basis function width (determining the response range).

Identification Process Implementation Data Preprocessing: Normalize input and output data to avoid network bias caused by dimensional differences Online Training: Dynamically adjust weights using gradient descent methods, or combine with K-means clustering to initialize basis function centers Performance Verification: Compare actual outputs with network-predicted outputs through Mean Squared Error (MSE) or fitted curves

MATLAB Simulation Key Points When implementing network training using `newrb` or custom programming, pay attention to tuning learning rates and momentum factors. Simulation results typically include: Identification error convergence curves Response comparisons under different excitation signals Robustness testing (such as generalization capability after adding noise)

For thesis reproduction, it is recommended to focus on optimization strategies for hidden layer node numbers and real-time improvements (such as combining recursive least squares method with RBF), as these details often become加分 points during thesis defense.