RBF Process Description

Architecture of RBF RBF network is an artificial neural network with an input layer, a hidden layer, and an output layer The Hidden layer of RBF consists of

RBF is a real-valued function that we use to calculate the distance between a variable with respect to a reference point

rbf RBF kernels are the most generalized form of kernelization and is one of the most widely used kernels due to its similarity to the Gaussian distribution

rbf RBF network is based on the cover theorem, it casts the data into a higher-dimensional space by using its hidden layer, therefore the number of neurons in the