We present a non-linear kernel-based version of the Recursive Least Squares (RLS) algorithm. Our Kernel-RLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum meansquared-error regressor. Sparsity (and therefore regularization) of the solution is achieved by an explicit greedy sparsification proces...

We start with estimation of a constant based on several noisy measurements. Suppose we have a resistor but do not know its resistance. So we measure it several times using a cheap (and noisy) multimeter. How do we come up with a good estimate of the resistance based on these noisy measurements? More formally, suppose x = (x1, x2, . . . , xn) T is a constant but unknown vector, and y = (y1, y2, ...

In this paper, a link gain prediction-based power control scheme is designed for DS-CDMA cellular mobile systems. The link gain prediction can help to remove the interference of the power control adjustment itself. The pipeline recurrent neural network (PRNN) with extended recursive least square (ERLS) is adopted for the prediction. This PRNN/ERLS predictor possesses infinite memory of past sig...

In this paper, the classical least squares (LS) and recursive least squares (RLS) for parameter estimation have been re-examined in the light of the present day computing capabilities. It has been demonstrated that for linear time-invariant systems, the performance of blockwise least squares (BLS) is always superior to that of RLS. In the context of parameter estimation for dynamic systems, the...

This thesis proposes and studies novel modifications to the least mean squares (LMS) and weighted recursive least squares (WRLS or weighted RLS) adaptive algorithms to estimate the impulse response of a wireless communications channel blindly without the aid of a training or probe sequence. Specifically, we use knowledge of receiver decision quality to weight the LMS and WRLS estimators to incr...