MATLAB Implementation of RBF Predictive Model

In this article, we introduce a novel approach called the RBF predictive model, which enables accurate prediction of RBF neural network functionality and characteristics. This method was developed through extensive data analysis and research, incorporating MATLAB code implementations for radial basis function center selection, width parameter optimization, and weight calculation algorithms. The model utilizes Gaussian basis functions and least-squares training methods to approximate complex nonlinear relationships in datasets. The implementation includes key MATLAB functions such as newrb() for network creation and sim() for prediction execution, allowing users to better understand and leverage RBF's potential through practical code examples. Beyond predicting RBF behavior, the model provides valuable insights for performance optimization through parameters like spread constants and maximum neuron counts. For researchers and engineers working with RBF networks, this predictive model serves as an essential tool for developing enhanced RBF applications, featuring customizable code structures for different dataset characteristics and prediction requirements.