Name
Abstract | ||
|---|---|---|
This paper studies two-stage recursive least squares identification problems for power signals by the decomposition technique. The basic idea is to decompose a power signal model into two submodels and then to identify the parameters of each submodel, respectively. Compared with the recursive least squares algorithm, the dimensions of the involved covariance matrices in each submodel become small and thus the proposed algorithm has a high computational efficiency. Finally, the simulation results indicate that the proposed algorithm is effective. |
Year | Venue | Keywords |
|---|---|---|
2015 | International Conference on Control Automation and Information Sciences | least squares,parameter estimation,two-stage algorithm,decomposition technique,power signals |
Field | DocType | ISSN |
Mathematical optimization,Control theory,Matrix (mathematics),Signal-to-noise ratio,Algorithm,Non-linear iterative partial least squares,Harmonic analysis,Estimation theory,Non-linear least squares,Recursive least squares filter,Mathematics,Covariance | Conference | 2475-7896 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangli Li | 1 | 2 | 1.40 |
| Lincheng Zhou | 2 | 0 | 0.68 |
| Peiyi Zhu | 3 | 21 | 5.78 |