Analysis on extended kernel recursive least squares algorithm
P Zhu, JC Principe - The 2013 International Joint Conference …, 2013 - ieeexplore.ieee.org
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013•ieeexplore.ieee.org
In this paper, the extended kernel recursive least squares (Ex-KRLS) algorithm is reviewed
and analyzed. We point out that the Theorem 1 in [10] is not always correct for general
cases. Furthermore, the Ex-KRLS algorithm for tracking model is just a random walk KRLS
algorithm. Finally, this algorithm is explained as a special Kalman filter in the reproducing
kernel Hilbert space.
and analyzed. We point out that the Theorem 1 in [10] is not always correct for general
cases. Furthermore, the Ex-KRLS algorithm for tracking model is just a random walk KRLS
algorithm. Finally, this algorithm is explained as a special Kalman filter in the reproducing
kernel Hilbert space.
In this paper, the extended kernel recursive least squares (Ex-KRLS) algorithm is reviewed and analyzed. We point out that the Theorem 1 in [10] is not always correct for general cases. Furthermore, the Ex-KRLS algorithm for tracking model is just a random walk KRLS algorithm. Finally, this algorithm is explained as a special Kalman filter in the reproducing kernel Hilbert space.
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