Kernel Recursive Least Squares With Multiple Feedback and Its Convergence Analysis

In kernel adaptive filters (KAFs), feedback network can lead to performance improvement from the aspects of estimation accuracy and convergence rate. In this brief, a novel feedback structure is developed and applied to the kernel recursive least squares (KRLS), generating the KRLS with multiple feedback (KRLS-MF). In the proposed KRLS-MF, multiple previous outputs are utilized to update the structure parameters in the recurrent form. The obtained parameters are also proved to be convergent. Compared with other KAFs with and without feedback, KRLS-MF can improve both the filtering accuracy and convergence rate, efficiently.

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