MATLAB Source Code for Radial Basis Function Neural Network Model Implementation

Radial Basis Function (RBF) neural networks serve as efficient nonlinear function approximation tools, widely applied in pattern recognition, time series prediction, and related fields. The core concept involves mapping input space to high-dimensional feature space through hidden layer radial basis functions (such as Gaussian functions), followed by linear output layer predictions for final results. Model Construction Logic Hidden Layer Design: Select radial basis functions (e.g., Gaussian kernel), determine center points (typically via K-means clustering or random sampling methods like datasample function), and set spread constants (bandwidth parameters using functions like std or range) to control function width and sensitivity. Weight Calculation: Hidden-to-output layer weights are commonly solved using pseudo-inverse methods (pinv function) or least squares optimization (lsqnonlin), ensuring minimized training error through matrix operations. Training Process: Implemented in two phases - unsupervised center learning (using kmeans clustering) followed by supervised weight optimization (backslash operator or linsolve), balancing model complexity and generalization capability. Implementation Key Points Data normalization (zscore or mapminmax) is essential to accommodate RBF function scale sensitivity. Spread constant selection (tuned via cvpartition cross-validation) critically affects model smoothness and performance. MATLAB's matrix operation advantages (vectorized computations using pdist2 for distance calculations) enable efficient hidden layer output computation. Extension Considerations Compared to Multi-Layer Perceptrons (MLP), RBF networks train faster but may require more hidden nodes as data complexity increases. Implement Orthogonal Least Squares (OLS) methods for node selection or incorporate regularization (ridge regression via ridge function) to prevent overfitting while maintaining model efficiency.