Radial Basis Function Networks (RBFNs)

In this article, we will talk about one of the algorithms that belong to the deep learning algorithms, RBFNs, as they are a special type of feeder neural network that use radial basis functions as activation functions. It has an input layer, a hidden layer, and an output layer and is mostly used for classification, regression, and time-series prediction. Radial basis function (RBF) networks are a common type of use in artificial neural networks for function approximation problems. Radial-based function networks are distinguished from other neural networks due to their global approximation and fast learning speed. The main advantage of the RBF network is that it has only one hidden layer and uses the radial basis function as the activation function.