unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

We investigate a class of hierarchical mixtures-of-experts (HME) models where generalized linear models with nonlinear mean functions of the form (+ x T) are mixed. Here () is the inverse link function. It is shown that mixtures of such mean functions can approximate a class of smooth functions of the form (h(x)) where h() 2 W 1 2;K (a Sobolev class over 0; 1] s), as the number of experts m in ...

We analyze in a regression setting the link between a scalar response and a functional predictor by means of a Functional Generalized Linear Model. We first give a theoretical framework and then discuss identifiability of the model. The functional coefficient of the model is estimated via penalized likelihood with spline approximation. The L2 rate of convergence of this estimator is given under...

Experiments to determine the potential for program-level and/or phase-level adaptation of branch predictor configuration for the purpose of total processor energy savings were performed. The performance and energyefficiency of an 8-wide issue, out-of-order processor with six different branch predictors were evaluated on the SPECcpu2000 benchmark suite. Each branch predictor was compared to the ...

In this paper we discuss the use of block principal pivoting and predictor-corrector methods for the solution of large-scale linear least squares problems with nonnegative variables (NVLSQ). We also describe two implementations of these algorithms that are based on the normal equations and corrected seminormal equations (CSNE) approaches. We show that the method of normal equations should be em...

It is an open question whether there is an interior-point algorithm for linear optimization problems with a lower iteration-complexity than the classical bound O( √ n log(1 μ0 )). This paper provides a negative answer to that question for a variant of the Mizuno-Todd-Ye predictor-corrector algorithm. In fact, we prove that for any > 0, there is a redundant Klee-Minty cube for which the aforemen...

The predictor-corrector methods P (EC) with equidistant discretization are applied to the numerical integration of a linear stochastic oscillator. Their ability in preserving the symplecticity, the linear growth property of the second moment, and the oscillation property of the solution of this stochastic system is studied. Their mean-square orders of convergence are discussed. Numerical experi...

In this paper we propose a new class of primal-dual path-following interior point algorithms for solving monotone linear complementarity problems. At each iteration, the method would select a target on the central path with a large update from the current iterate, and then the Newton method is used to get the search directions, followed by adaptively choosing the step sizes, which are e.g. the ...

This paper deals with a novel method of continuous-time model-predictive control for nonlinear time-delayed systems. The problems relating to time delays are solved by incorporating the Smith-predictor scheme in a control-law derivation. A nonlinear-mapping approximation, employing either continuous piece-wise linear functions or a fuzzy system, is also an integral part of the control scheme, a...

We present a method for designing efficient multigenic predictors with few probes and its application to the prediction of the response to preoperative chemotherapy in breast cancer. In this study, each DNA probe was regarded as an elementary predictor of the response to the chemotherapy and the probes which were selected performed a faithful sampling of the training dataset. In a first stage o...