Radial Basis Function Network

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. A radial basis function network is an artificial neural network that uses radial basis functions as activation functions. It is a linear combination of radial basis functions. They are used in function approximation, time series prediction, and control. In a RBF network there are three types of parameters that need to be chosen to adapt the network for a particular task: the center vectors mathbf c_i, the output weights wi, and the RBF width parameters i. In the sequential training of the weights are updated at each time step as data streams in.

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High Quality Content by WIKIPEDIA articles! A radial basis function network is an artificial neural network that uses radial basis functions as activation functions. It is a linear combination of radial basis functions. They are used in function approximation, time series prediction, and control. In a RBF network there are three types of parameters that need to be chosen to adapt the network for a particular task: the center vectors mathbf c_i, the output weights wi, and the RBF width parameters ßi. In the sequential training of the weights are updated at each time step as data streams in.