In this thesis, we consider the problem of the nonparametric estimation of the regression function when the response variable is real and the regressor is valued in a functional space (space of infinite dimension), by using ...
If a problem has a unique solution, but this solution is not continuous with
respect to small variations of data this problem is ill posed. The methods used to
stabilize ill-posed problems are known to stabilize.
The ...
In this work we presented the performances of a new class of evolutionary
algorithms called chaotic optimization algorithm (COA). Proposed to solve
nonlinear optimization problems with bounded variables by Caponetto et ...
Artificial neural networks (ANN) are universal approximators that allow to express the
correlation between input data and output data. Learning by ANN is based on the adaptation of the free parameters of the network by ...
The study of a link between two variables was and still a challenge for a lot of researchers in many fields of application and as in many of these fields appear functional data, we find many works have been devoted in this ...
The goal of this work is the study of a class of inverse Cauchy problems. A new regularization method based on the well-known method of regularization by truncation of eliminating all high frequencies in the solution of ...