A modification to the OE IIR system structure is proposed to ensure global convergence for sufficient modeling of the unknown system. The proposed structure is effectively equivalent to whitening the input signal before being applied to the original OE setup. This guarantees the unimodality of the error surface for sufficient modeling. An adaptive update scheme for the new structure is derived ...

Adaptive least mean square (LMS) filters with or without training sequences, which are known as training-based and blind detectors respectively, have been formulated to counter interference in CDMA systems. The convergence characteristics of these two LMS detectors are analyzed and compared in this paper. We show that the blind detector is superior to the training-based detector with respect to...

The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input auto-correlation matrix is ill-conditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the ad...

This paper defines haw the quality of approximation used in the error transfer function analysis affects the accuracy of the resulting steady-state mean square error (MSE) of the Least-Mean-Square (LMS) adaptive filters. It shows that the error transfer function approach is very accurate for extremely narrow bandwidth input signals, and it deteriorates as the bandwidth of input signal increases.

In general, classical methods such as maximum likelihood (ML) and least squares (LS) estimation methods are used to estimate the shape parameters of the Burr XII distribution. However, these estimators are very sensitive to the outliers. To overcome this problem we propose alternative robust estimators based on the M-estimation method for the shape parameters of the Burr XII distribution. We pr...

This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise and the estimates is unbounded. The bound applies to many loss functions besides the squared loss, and scales only with the sub-Gaussian or subexponential parameters without further statistical assumptions such as the...

The usual assumption behind robust design is that the number of replicates at each design point during an experimental stage is equal. In practice, however, it is often the case that this assumption is not met due to physical limitations and/or cost constraints. In this situation, using the usual method of ordinary least squares (OLS) to obtain fitted response functions for the mean and varianc...

T HE introduction by Hoerl and Kennard (1970) of a ridge regression estimator to deal with the problem of multicollinearity in regression has been followed by a large number of papers in the statistical literature. In the area of econometrics, though, the method of ridge regression has received little attention. I One of the reasons for the lack of interest in ridge regression on the part of th...

Mango is a kind of popular tropical fruit, and the soluble solid content is an important in this study visible and short-wave near-infrared spectroscopy (VIS/SWNIR) technique was applied. For sake of investigating the feasibility of using VIS/SWNIR spectroscopy to measure the soluble solid content in mango, and validating the performance of selected sensitive bands, for the calibration set was ...

The paper starts with a brief review of methods of model-tting using empirical transforms. We then present a method for estimating the parameters in indexed stochastic models via a least-squares approach based on empirical transforms. Asymptotic approximations are derived for the mean squared error and for the distribution of the resulting estimator. The explicit expression for the mean squared...