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• W1994833873 abstract "Feedforward neural networks with a single hidden layer using normalized gaussian units are studied. It is proved that such neural networks are capable of universal approximation in a satisfactory sense. Then, a hybrid learning rule as per Moody and Darken that combines unsupervised learning of hidden units and supervised learning of output units is considered. By using the method of ordinary differential equations for adaptive algorithms (ODE method) it is shown that the asymptotic properties of the learning rule may be studied in terms of an autonomous cascade of dynamical systems. Some recent results from Hirsch about cascades are used to show the asymptotic stability of the learning rule." @default.
• W1994833873 created "2016-06-24" @default.
• W1994833873 creator A5011075211 @default.
• W1994833873 date "1994-03-01" @default.
• W1994833873 modified "2023-09-26" @default.
• W1994833873 title "On Functional Approximation with Normalized Gaussian Units" @default.
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• W1994833873 doi "https://doi.org/10.1162/neco.1994.6.2.319" @default.
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