The architecture of this Radial Basis Function Network is fundamentally different from that of most neural network architectures. Most neural network architectures have many layers and introduce nonlinearity by repeatedly applying nonlinear activation functions. In contrast, a Radial Basis Function Network network has only an input layer, a single hidden layer, and an output layer. The input layer is not a computation layer; it simply receives input data and feeds it into the RBF network's special hidden layer.
