MATLAB Implementation of RBF Neural Network with Source Code

This repository provides MATLAB-based source code for implementing Radial Basis Function (RBF) neural networks. The source code fully implements RBF neural network functionality, making it suitable for various data processing and pattern recognition applications. The implementation includes core algorithms for RBF center selection using k-means clustering or random sampling methods, Gaussian basis function calculation with adjustable spread parameters, and output layer weight optimization through linear regression or gradient descent approaches. Key functions include network initialization, training with epoch control, prediction methods, and performance evaluation metrics. RBF neural networks are particularly effective for nonlinear modeling tasks due to their localized basis functions and fast training characteristics. This source code allows users to customize critical parameters such as the number of hidden neurons, learning rate, activation function spreads, and training iterations to adapt to different datasets and application requirements. The code structure follows modular design principles with separate functions for data preprocessing, network training, and validation. Before using this code, ensure you have MATLAB installed and basic familiarity with MATLAB programming syntax. The implementation includes comments explaining each computational step and provides examples for quick integration into existing projects.