Usually neuro-fuzzy networks are trained by using mean squared error (MSE) as cost function. This approach may lead to some problems when the network is used to classify patterns. In this paper, we propose a new empirical risk functional that is particularly suitable for classification tasks, when neuro-fuzzy learning is based on a gradient descent strategy. This functional has the properties o...

A large number of pilots are utilized to acquire channel information in traditional channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) system, which leads to lower spectrum efficiency. For exploiting the sparse channel characteristics of 3GPP multipath channels, we employ the Compressed Sensing (CS) approach for channel estimation. Two CS-based recovery algorithms, Orthogo...

Sensitivity analysis for random measurement error can be applied in the absence of validation data by means regression calibration and simulation-extrapolation. These have not been compared this purpose. A simulation study was conducted comparing performance simulation-extrapolation linear logistic regression. The two methods evaluated terms bias, mean squared (MSE) confidence interval coverage...

Estimation of small area means in the presence of area level auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. The assumptions on the area level regression are considerably weaker than standard small area models. Both the small area mean functions and the between area variance function are modeled as smooth functions of area level covar...

In this paper, we solve the sum mean-squared error (MSE)optimal 1-bit quantized precoding problem exactly for smallto-moderate sized multiuser multiple-input multiple-output (MU-MIMO) systems via branch and bound. To this end, we reformulate the original NP-hard precoding problem as a tree search and deploy a number of strategies that improve the pruning efficiency without sacrificing optimalit...

Zhang and Shaman considered the problem of estimating the conditional mean-squared prediciton error (CMSPE) for a Gaussian autoregressive (AR) process. They used the final prediction error (FPE) of Akaike to estimate CMSPE and proposed that FPE’s effectiveness be judged by its asymptotic correlation with CMSPE. However, as pointed out by Kabaila and He, the derivation of this correlation by Zha...

The LMS adaptive algorithm is the most popular algorithm for adaptive ltering because of its simplicity and robustness. However, its main drawback is slow convergence whenever the adaptive lter input auto-correlation matrix is ill-conditioned i.e. the eigenvalue spread of this matrix is large 2, 4]. Our goal in this paper is to develop an adaptive signal transformation which can be used to spee...

This article investigates estimators of a weighted average of stratum-specific univariate parameters and compares them in terms of a design-based estimate of mean-squared error (MSE). The research is motivated by a stratified survey sample of Florida Medicaid beneficiaries, in which the parameters are population stratum means and the weights are known and determined by the population sampling f...