Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TNN.2011.2116803 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
certain error,neural networks,estimation technique,measurement equations,nonlinear parameterized approximating functions,minimization,moving horizon,state estimation,nonlinear systems,sliding-window least-squares cost function,nonlinear discrete-time system,least squares approximations,bounded noise,nonlinear control systems,nonlinear discrete-time systems,feedforward neural network,measurement equation,discrete time systems,bounded noises,minimisation,offline optimization,cost function,neural nets,moving-horizon state estimation,mh estimation scheme,approximation algorithms,extended kalman filter,mathematical model,nonlinear system,neural network,estimation,kalman filters,artificial neural networks | Approximation algorithm,Extended Kalman filter,Mathematical optimization,Parameterized complexity,Nonlinear system,Computer science,Control theory,Kalman filter,Artificial neural network,Estimator,Bounded function | Journal |
Volume | Issue | ISSN |
22 | 5 | 1941-0093 |
Citations | PageRank | References |
7 | 0.63 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Alessandri | 1 | 312 | 27.63 |
| M. Baglietto | 2 | 288 | 23.19 |
| G. Battistelli | 3 | 171 | 11.88 |
| M. Gaggero | 4 | 41 | 3.30 |