Computational Application of Radial Basis Function Neural Networks (RBFNN) which employ radial basis functions in hidden layers, efficiently modeling complex nonlinear relationships in data. Their unique architecture enables accurate function approximation, classification, and regression, making them versatile and effective across multiple domains.