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Istituto di Studi sui Sistemi Intelligenti per l'Automazione     
Alessandri A., Baglietto M., Parisini T., Zoppoli R. A Neural State Estimator with Bounded Errors for Nonlinear Systems. In: Ieee Transactions on Automatic Control, vol. 44 (11) pp. 2028 - 2042. IEEE, 1999.
 
 
Abstract
(English)
A neural state estimator is described, acting on discrete-time nonlinear systems with noisy measurement channels. A sliding-window quadratic estimation cost function is considered and the measurement noise is assumed to be additive. No probabilistic assumptions are made on the measurement noise nor on the initial state. Novel theoretical convergence results are developed for the error bounds of both the optimal and the neural approximate estimators. To ensure the convergence properties of the neural estimator, a minimax tuning technique is used. The approximate estimator can be designed off line in such a way as to enable it to process on line any possible measure pattern almost instantly.
DOI: 10.1109/9.802911
Subject Bounded error state estimation
Discrete-time nonlinear systems
Neural network


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