RBF Networks

RBF (Radial Basis Function) Networks are a type of artificial neural network used for pattern recognition, classification, and approximation tasks. They consist of an input layer, a hidden layer with neurons that use a radial basis function (often a Gaussian), and an output layer. The hidden neurons respond strongly to inputs close to their center point, acting like localized filters. The network learns by adjusting these centers and the weights to map inputs to correct outputs. RBF networks are valued for their speed and ability to model complex, nonlinear relationships in data.