Radial Basis Function Neural Network (RBF) - MATLAB Implementation with Detailed Code Examples

This document presents a detailed MATLAB implementation of the Radial Basis Function Neural Network (RBF), designed to assist learners in understanding and applying RBF neural networks. The program begins with fundamental concepts and theoretical principles of RBF networks, explaining the mathematical foundation including Gaussian activation functions and weight optimization techniques. The implementation section provides step-by-step MATLAB code demonstrations covering core components: data preprocessing, hidden layer initialization using k-means clustering for center selection, Gaussian radial basis function calculation with adjustable spread parameters, and output layer training through linear regression or gradient descent methods. Key MATLAB functions highlighted include 'newrb' for network creation, 'radbas' for activation functions, and custom code for parameter tuning. Practical application examples demonstrate RBF networks solving regression and classification problems, with performance comparisons against traditional MLP networks. The code includes error analysis modules using MSE metrics and visualization tools for decision boundaries. Common implementation challenges and troubleshooting guidelines address issues like overfitting prevention through regularization and optimal hidden node determination. This comprehensive reference combines theoretical foundations with practical MATLAB implementation strategies, enabling learners to master RBF network design, parameter optimization, and real-world application deployment.