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The approximation of discontinuous multivariate functions from a set of scattered data points is usually a two-stage process: ÿrst, a detection algorithm is applied to localize the discontinuity sets, then the functions are reconstructed using a ÿtting method. In this paper we propose a new method for the second stage, based on the computation of discrete(More)
The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent(More)
Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with(More)
In this paper we present an approximation method of surfaces by a new type of splines, which we call fairness bicubic splines, from a given Lagrangian data set. An approximating problem of surface is obtained by minimizing a quadratic functional in a parametric space of bicubic splines. The existence and uniqueness of this problem are shown as long as a(More)
This paper deals with a construction problem of free-form curves from data constituted by some approximation points and a boundary value problem for an ordinary differential equation (ODE). The solution of this problem is called an ODE curve. We discretize the problem in a space of B-spline functions. Finally, we analyze a graphical example in order to(More)
SUMMARY Reducing the dimensionality of the raw input variable space is an important step in pattern recognition and functional approximation tasks often determined by practical feasibility. The purpose of this study was to investigate an information theoretic approach to feature selection. We will use mutual information (MI) as a pre-processing step for(More)
The European Space of Higher Education (ESHE) is a new conceptual formulation of the organization of teaching at the university, largely involving the development of new training models based on the individual student's work. In this context, the University of Granada has approved two plans of Educational Excellence to promote a culture of quality and(More)