Modeling RBF Network with Simulation of Training Curves and Fitting Curves

RBF (Radial Basis Function) networks are widely used neural network architectures renowned for their excellent performance in function approximation and nonlinear classification problems. Modeling RBF networks in MATLAB and simulating their training and fitting processes effectively demonstrate their capabilities.

RBF Network Simulation Steps Data Preparation: First, prepare training and testing datasets. Training data adjusts network weights while testing data validates generalization capability. Ensure proper data distribution to prevent overfitting or underfitting. Network Construction: Use MATLAB's `newrb` or `newrbe` functions to create RBF networks. `newrb` incrementally adds hidden layer neurons whereas `newrbe` determines all neurons in a single training epoch. Selecting appropriate spread parameters critically impacts network fitting performance. Training Process: During RBF network training, MATLAB adjusts weights between hidden and output layers. Training curves record error evolution across iterations, indicating convergence status. Smoothly descending curves signify effective learning. Fitting Verification: Use test data to validate network fitting capability post-training. Fitting curves illustrate proximity between predicted and actual values. Well-trained RBF networks should accurately approximate target functions with high curve alignment.

Key Observation Points Training Curves: Monitor whether errors decrease rapidly and stabilize. Oscillations or persistent high errors may indicate improper spread settings or significant data noise. Fitting Curves: Evaluate prediction performance on test data. Overfitting manifests as low training errors but high testing errors, while underfitting shows poor performance in both metrics.

By appropriately tuning RBF network parameters (such as spread and hidden neuron count), optimal training and fitting results can be achieved in MATLAB.