Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5060360 | Economics Letters | 2013 | 4 Pages |
Abstract
⺠The correspondence between learning algorithms and the Kalman filter is generalized. ⺠Both least squares and stochastic gradient are covered in the same united framework. ⺠Our approach derives these correspondences from the algorithms' non-recursive forms. ⺠The algorithms' gains are allowed to be (unrestrictedly) time-varying. ⺠Our correspondences hold exactly, instead of asymptotically approximated.
Related Topics
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Authors
Michele Berardi, Jaqueson K. Galimberti,