Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6957306 | Signal Processing | 2018 | 14 Pages |
Abstract
This paper derives a new model for the stochastic behavior of the Gaussian KLMS algorithm. The analysis considers the possibility of time correlated input vectors, a situation that cannot be modeled by existing models. Recursions are derived which predict both the transient and the steady-state behaviors of the algorithm for a time-varying dictionary. The model predictions show excellent agreement with Monte Carlo simulations in both modes of operation, providing significant improvement when compared to the accuracy of existing models.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Wemerson D. Parreira, Márcio H. Costa, José C.M. Bermudez,