Name
Title | ||
|---|---|---|
Recursive inverse basis function (RIBF) algorithm for identification of periodically varying systems |
Abstract | ||
|---|---|---|
This paper presents a new algorithm for the identification (tracking) of periodically varying systems. When the system coefficients vary rapidly, conventional adaptive estimators such as the least mean squares (LMS) and the weighted least squares (WLS) algorithms become inefficient. Basis function (BF) algorithms have shown superiority over the conventional ones in tracking the parameters of periodically varying systems. Unfortunately, BF estimators are computationally very demanding. A new recursive inverse basis function estimator (RIBF) and its frequency-adaptive version are proposed which provides a significant reduction in the computational complexity and the mean square parameter estimation error without the need for any error correction code. |
Year | Venue | Keywords |
|---|---|---|
2012 | Signal Processing Conference | computational complexity,inverse problems,least mean squares methods,parameter estimation,computational complexity,frequency-adaptive RIBF,least mean squares,mean square parameter estimation error,periodically varying system identification,recursive inverse basis function algorithm,weighted least squares,Basis function algorithms,adaptive filters,nonstationary process,periodically varying systems,system identification |
Field | DocType | ISSN |
Least squares,Least mean squares filter,Algorithm,Adaptive filter,Basis function,Non-linear least squares,System identification,Recursive least squares filter,Mathematics,Estimator | Conference | 2219-5491 |
ISBN | Citations | PageRank |
978-1-4673-1068-0 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qadri Mayyala | 1 | 5 | 2.11 |
| Osman Kukrer | 2 | 0 | 0.34 |
| Aykut Hocanin | 3 | 2 | 0.73 |