Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
696676 | Automatica | 2011 | 6 Pages |
Abstract
In this paper, we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted (windowed) least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. We optimize the window shape for a certain class of parameter variations and we derive computationally attractive recursive smoothing algorithms for such an optimized case.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Maciej Niedźwiecki, Szymon Gackowski,