Dominique Bertin

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Neural networks are well known for their ability to model non linear functions, but as statistical methods usually does, they use a no parametric approach thus, a priori knowledge is not obvious to be taken into account no more than the a posteriori knowledge. In order to deal with these problematics, an original way to encode the knowledge inside the(More)
Neural networks are increasingly used in the field of hydrology due to their properties of parsimony and universal approximation with regard to nonlinear systems. Nevertheless, as a result of the non stationarity of natural variables (rainfalls and consequently discharges) it appeared as difficult to capture both dynamics (roughly slow and fast) in a same(More)
The ability of the multilayer perceptron to model the inverse relation of a fictitious watershed is investigated. Comparison is done between a new formulation of data assimilation and the standard multilayer perceptron applied to three kinds of models: static, feedforward and recurrent. It appears that both techniques are equivalent and allow a very good(More)
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