Alessandro Grassia

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— Observers design is addressed for a class of continuous-time, nonlinear dynamic systems with Lipschitz nonlinearities. A full-order state estimator is considered that depends on an innovation function made up of two terms: a linear gain and a feedforward neural network that provides a nonlinear contribution. The gain and the weights of the neural network(More)
Many pathologies can be identified by evaluating differences raised in the physical parameters of involved tissues. In a Magnetic Resonance Imaging (MRI) framework, spin-lattice T1 and spin-spin T2 relaxation time parameters play a major role in such an identification. In this manuscript, a theoretical study related to the evaluation of the achievable(More)
An approach based on optimization is described to construct state estimators that provide a stable dynamics of the estimation error and minimize a L p measure of the estimation error. The state estimator depends on an innovation function made up of two terms: a linear gain and a feedforward neural network. The gain and the weights of the neural network can(More)
— A method for the identification of a freeway macroscopic model is presented. The model is based on the idea of dividing a freeway trunk in sections covered by the cells of the wireless network and associated with state variables describing density of vehicles, mean velocity, and percentage of active mobile phones. Using the density and hand-off(More)
The increase in container transport and the restricted capacity of existing port facilities determine a large number of issues, including terminal congestion, delivery delay, and economic loss. This motivates research activities aimed at studying the factors that influence performance and economic profit [1]. Toward this end, one has to rely on a model that(More)
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