Radial Basis Function Networks (RBFNs) are a class of artificial neural networks that use radial basis functions as activation functions. They are particularly effective in function approximation, pattern recognition, time-series prediction, and control systems. RBF networks typically consist of three layers: an input layer, a hidden layer with a radial basis activation function (commonly Gaussian), and a linear output layer. Their ability to approximate complex, non-linear mappings and their interpretability make them popular in various fields.Radial Basis Function Networks (RBFNs) offer exciting possibilities in areas such as control systems, time-series forecasting, high-dimensional data analysis, and deep learning. These projects involve advancing both theoretical and practical aspects of RBFNs, such as improving generalization, adapting to new environments, and scaling to large datasets. With their ability to approximate complex functions and flexibility in various domains, RBFNs remain a rich area for research and innovation.