The selection of neural models of nonlinear dynamical systems by statistical tests

A procedure for the selection of neural models of dynamical processes is presented. It uses statistical tests at various levels of model reduction, in order to provide optimal tradeoffs between accuracy and parsimony. The efficiency of the method is illustrated by the modeling of a highly nonlinear NARX process.<
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