The aim of this thesis is to present a novel mode of convergence of sequences of random variables, and to establish a better complete convergence rate's for kernel estimators of functional characteristic’s of unknown ...
The objective of this work is to study discrete dynamical systems, introducing fundamental concepts such as stability, bifurcation, and chaos. We will then apply these concepts to a
discrete-time predator-prey system with ...
This work provides insight into the application of the Galerkin method for solving
two boundary value problems. The first problem deals with bidimensional linear
Schrödinger parabolic partial differential equations, and ...
The present work is basically devoted to study the existence and uniqueness of solutions
for certain classes of nonlinear fractional differential equations with initial conditions via
the ψ−Caputo fractional derivative ...
Statistical distributions are widely applied to describe di§erent real world phenomena. As a result of the usefulness of statistical distributions, many researchers have
studied their theory extensively and new distributions ...
Statistical distributions are widely applied to describe di§erent real world phenomena. As a result of the usefulness of statistical distributions, many researchers have
studied their theory extensively and new distributions ...
This work presents a program for calculating the Value at Risk (VaR) using the
Extreme Value Theory (EVT), a statistical theory that focuses on studying rare
cases. EVT has numerous applications, and in this study, we ...
This work draws new results on stabilization and on some types of synchronization applied to chaotic systems when the parameters are unknown. First, we build an adaptive controller to stabilize the chaotic system with a ...
The 𝑘-out-of-𝑛 system represents a redundancy structure widely used in reliability engineering. It is a system that fails (or operates) if and only if at least k of its components
fail (or operate). This type of system ...
The present work is devoted on the one hand, to the study of some transmission problems and it consists mainly in the study a differential equation in a domain that contains one or more discontinuity points, with the ...
Gaussian process (GP) is a stochastic process that has been successfully applied in finance, black-box modeling of biosystems, machine learning, geostatistics, multitask learning or robotics and reinforcement learning. ...
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 ...