In this paper one approach is proposed for using wavelets in non parametric regression estimation. The proposed non parametric estimator, named wavelet network, has a neural network like structure, but consists of wavelets. It makes use of techniques of regressor selection completed with backpropagation procedures. It is capable of handling nonlinear regressions of moderately large input dimensio with sparse training data. Numerical examples are reported to illustrate the performance of this proposed approach
Abstract—A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In t...
Programme 5 - Traitement du signal, automatique et productique.Projet ASAvailable at INIST (FR), Doc...
A bstract The wavelet transform was introduced in the 1980’s and it was developed as an alternative ...
In this paper one approach is proposed for using wavelets in non parametric regression estimation. T...
Radial wavelet networks have recently been proposed as a method for nonparametric regression. In thi...
This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term....
The wavelet network has been introduced as a special feedforward neural network supported by the wav...
A new methodology for the application of wavelets in non-parametric density estimation is proposed. ...
Semiparametric regression models have a linear part as in the linear regression and a nonlinear part...
We consider the problem of estimating the relationship between a response variable and a set of expl...
The semi-parametric regression model combines parametric and nonparametric regression. However, non-...
Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-...
We show that a nonparametric estimator of a regression function, obtained as solution of a specific ...
Regression analysis is an essential tools in most research fields such as signal processing, economi...
This paper presents a new non-parametric modeling technique. The method is simple and yet efficient ...
Abstract—A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In t...
Programme 5 - Traitement du signal, automatique et productique.Projet ASAvailable at INIST (FR), Doc...
A bstract The wavelet transform was introduced in the 1980’s and it was developed as an alternative ...
In this paper one approach is proposed for using wavelets in non parametric regression estimation. T...
Radial wavelet networks have recently been proposed as a method for nonparametric regression. In thi...
This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term....
The wavelet network has been introduced as a special feedforward neural network supported by the wav...
A new methodology for the application of wavelets in non-parametric density estimation is proposed. ...
Semiparametric regression models have a linear part as in the linear regression and a nonlinear part...
We consider the problem of estimating the relationship between a response variable and a set of expl...
The semi-parametric regression model combines parametric and nonparametric regression. However, non-...
Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-...
We show that a nonparametric estimator of a regression function, obtained as solution of a specific ...
Regression analysis is an essential tools in most research fields such as signal processing, economi...
This paper presents a new non-parametric modeling technique. The method is simple and yet efficient ...
Abstract—A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In t...
Programme 5 - Traitement du signal, automatique et productique.Projet ASAvailable at INIST (FR), Doc...
A bstract The wavelet transform was introduced in the 1980’s and it was developed as an alternative ...