MATLAB Implementation for Training RBF Neural Network Using Data Center Clustering Method

This article discusses how to implement Radial Basis Function (RBF) network training in MATLAB using the data center clustering method. The implementation specifically avoids using MATLAB's built-in neural network training functions, instead employing custom algorithms to achieve successful training results. This approach involves key steps such as calculating Euclidean distances for cluster center selection, determining optimal spread parameters for Gaussian functions, and computing weight matrices using linear regression methods. The implementation provides valuable insights for professionals working with neural networks and machine learning, as it demonstrates advanced techniques for network initialization and parameter optimization. The custom clustering algorithm first identifies optimal center points from the input data using k-means or similar methods, then calculates appropriate widths for each radial basis function based on the distribution of data points around each center. Finally, the output weights are determined using pseudoinverse matrix calculations to minimize mean squared error. This work offers significant value for researchers seeking deeper understanding of neural network implementations and provides useful foundations for future research projects in pattern recognition and function approximation applications.