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Nonparametric estimation of uncertain components in nonlinear dynamical system modelling

A nonparametric identification of uncertain components in the modelling of time varying stochastic autoregressive nonlinear processes is proposed. A kernel estimator of the uncertain component is defined, whose almost sure convergence has been established under mild conditions. This approach is particularly well adapted to the identification of kinetic components in the modelling of biotechnological processes.