title: Moving-horizon state estimation for nonlinear systems using neural networks.
abstract: Moving-horizon (MH) state estimation is addressed for nonlinear discrete-time systems affected by bounded noises acting on system and measurement equations by minimizing a sliding-window least-squares cost function. Such a problem is solved by searching for suboptimal solutions for which a certain error is allowed in the minimization of the cost function. Nonlinear parameterized approximating functions such as feedforward neural networks are employed for the purpose of design. Thanks to the offline optimization of the parameters, the resulting MH estimation scheme requires a reduced online computational effort. Simulation results are presented to show the effectiveness of the proposed approach in comparison with other estimation techniques.
authors.affiliation: Department of Production Engineering, Thermoenergetics,and Mathematical Models, University of Genoa, Genova, Italy. alessandri@diptem.unige.it
authors.forename: Angelo
authors.initials: A
authors.lastname: Alessandri
authors.forename: Marco
authors.initials: M
authors.lastname: Baglietto
authors.forename: Giorgio
authors.initials: G
authors.lastname: Battistelli
authors.forename: Mauro
authors.initials: M
authors.lastname: Gaggero
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation / Q000592:standards; D055641:Mathematical Concepts; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011340:Problem Solving