Name
Abstract | ||
|---|---|---|
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 offline in such a way as to enable it to process on line any possible measure pattern almost instantly. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/9.802911 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
State estimation,Nonlinear systems,Minimax techniques,Cost function,Noise measurement,Additive noise,Convergence,Control systems,Stochastic processes,Observers | Efficient estimator,Minimum-variance unbiased estimator,Mathematical optimization,Minimax,Stein's unbiased risk estimate,Control theory,Minimax estimator,Invariant estimator,Mathematics,Estimator,Consistent estimator | Journal |
Volume | Issue | ISSN |
44 | 11 | 0018-9286 |
Citations | PageRank | References |
35 | 3.97 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Alessandri | 1 | 312 | 27.63 |
| M. Baglietto | 2 | 288 | 23.19 |
| T Parisini | 3 | 935 | 113.17 |
| R. Zoppoli | 4 | 279 | 51.51 |