wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual demrbf1 wolffd@0: wolffd@0: wolffd@0: wolffd@0:

demrbf1 wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Demonstrate simple regression using a radial basis function network. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: demrbf1

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wolffd@0: Description wolffd@0:

wolffd@0: The problem consists of one input variable x and one target variable wolffd@0: t with data generated by sampling x at equal intervals and then wolffd@0: generating target data by computing sin(2*pi*x) and adding Gaussian wolffd@0: noise. This data is the same as that used in demmlp1. wolffd@0: wolffd@0:

Three different RBF networks (with different activation functions) wolffd@0: are trained in two stages. First, a Gaussian mixture model is trained using wolffd@0: the EM algorithm, and the centres of this model are used to set the centres wolffd@0: of the RBF. Second, the output weights (and biases) are determined using the wolffd@0: pseudo-inverse of the design matrix. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: demmlp1, rbf, rbffwd, gmm, gmmem

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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