Abstract
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.
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