Adaptive bilinear predictors

This paper considers an extended recursive least squares (RLS) adaptive bilinear predictor. It is shown that the extended RLS adaptive bilinear predictor is guaranteed to be stable in the sense that the time average of the squared a-posteriori prediction error signal is bounded whenever the input signal is bounded in the same sense. It also shows that the a-priori prediction error itself is bounded whenever the desired signal is bounded. This paper also contains simulation results to demonstrate the usefulness of the extended RLS adaptive bilinear predictor.<
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