Massimo Battisti

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The minimisation of a least mean squares cost function produces poor results in the ranges of the input variable where the quantity to be approximated takes on relatively low values. This can be a problem if an accurate approximation is required in a wide dynamic range. The present paper approaches this problem in the case of multilayer perceptrons trained(More)
Training a feedforward neural network leads to a model affected by uncertainty. A measure of this uncertainty gives an important indication of neural model’s quality and of its generalisation characteristics. In the present paper we propose an original procedure to give a quantitative description of the uncertainty that derives from the limited size of the(More)
The paper addresses the problem of deening a neural system which combines pieces of independent information available in both the data and parameters spaces. The problem is approached in the framework of the probabilistic interpretation of neural modelling: in order to take into account the indetermination associated to the training process, a distribution(More)
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