Abstract
We describe certain results of Mhaskar concerning the approximation capabilities of neural networks with one hidden layer. In particular, these results demonstrate the construction of neural networks evaluating a squashing function or a radial basis function for optimal approximation of the Sobolev spaces. We also report on the application of some of these ideas in the construction of general-purpose networks for the prediction of time series, when the number of independent variables is known in advance, such as the Mackey-Glass series or the flour data.
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