Name
Title | ||
|---|---|---|
Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering |
Abstract | ||
|---|---|---|
This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s11045-017-0491-y | Multidim. Syst. Sign. Process. |
Keywords | Field | DocType |
Parameter estimation,Least squares,Data filtering,Multivariate system,Nonlinear system,Pseudo-linear system | Least squares,Autoregressive model,Mathematical optimization,Multivariate statistics,Iteratively reweighted least squares,Generalized least squares,Non-linear least squares,Total least squares,Mathematics,Recursive least squares filter | Journal |
Volume | Issue | ISSN |
29 | 3 | 0923-6082 |
Citations | PageRank | References |
3 | 0.38 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Ma | 1 | 33 | 4.85 |
| Feng Ding | 2 | 4973 | 231.42 |
| Ahmed Alsaedi | 3 | 48 | 12.75 |
| Tasawar Hayat | 4 | 999 | 71.98 |