Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demrbf1 Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demrbf1 Daniel@0:

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Daniel@0: Purpose Daniel@0:

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

Daniel@0: Synopsis Daniel@0:

Daniel@0:

Daniel@0: demrbf1

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Daniel@0: Description Daniel@0:

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

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

Daniel@0: See Also Daniel@0:

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

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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