Three MATLAB Radial Basis Function Neural Network Programs: Cluster-Based, Gradient-Based, and OLS-Based Implementations

This documentation presents three MATLAB implementations of Radial Basis Function (RBF) neural networks developed by the author. These programs implement cluster-based RBF, gradient descent-based RBF, and OLS-based RBF neural networks, providing researchers with practical tools for understanding and processing data with nonlinear relationships. The cluster-based RBF algorithm utilizes clustering techniques (typically K-means or similar methods) to determine the optimal number and positioning of radial basis function centers in the hidden layer. This approach enhances prediction accuracy by automatically adapting the network architecture to the data distribution through MATLAB's clustering functions and centroid calculation routines. The gradient-based RBF implementation employs gradient descent optimization to fine-tune the parameters of radial basis functions, including center positions, widths, and output weights. The MATLAB code likely implements backpropagation-like parameter updates using derivative calculations and learning rate adjustments to improve the model's predictive capability through iterative refinement. The OLS-based RBF neural network applies Orthogonal Least Squares methodology to optimize radial basis function parameters, particularly focusing on efficient center selection and weight calculation. This approach enhances model robustness and prediction performance by leveraging MATLAB's matrix operations and orthogonal decomposition techniques to minimize reconstruction error while maintaining numerical stability. Collectively, these three programs provide valuable resources for handling nonlinear data and performing predictive analysis, with each implementation demonstrating distinct advantages in training efficiency, parameter optimization, and architectural adaptability through well-structured MATLAB code featuring clear function modularization and algorithmic step implementations.