Results 11 to 20 of about 7,587 (302)

Reproducing kernel Hilbert space method for the solutions of generalized Kuramoto–Sivashinsky equation

open access: yesJournal of Taibah University for Science, 2019
Reproducing kernel Hilbert space method is given for the solution of generalized Kuramoto–Sivashinsky equation. Reproducing kernel functions are obtained to get the solution of the generalized Kuramoto–Sivashinsky equation.
Ali Akgül, Ebenezer Bonyah
doaj   +2 more sources

Reproducing Kernel Hilbert Space vs. Frame Estimates

open access: yesMathematics, 2015
We consider conditions on a given system F of vectors in Hilbert space H, forming a frame, which turn H into a reproducing kernel Hilbert space. It is assumed that the vectors in F are functions on some set Ω .
Palle E. T. Jorgensen, Myung-Sin Song
doaj   +2 more sources

Reproducing Kernel Kreĭn Spaces [PDF]

open access: yes, 2014
This chapter is an introduction to reproducing kernel Kre?in spaces and their interplay with operator valued Hermitian kernels. Existence and uniqueness properties are carefully reviewed. The approach used in this survey involves the more abstract, but very useful, concept of linearization or Kolmogorov decomposition, as well as the underlying concepts
Aurelian Gheondea, Gheondea, Aurelian
openaire   +4 more sources

Reproducing kernel functions for the generalized Kuramoto-Sivashinsky equation

open access: yesITM Web of Conferences, 2018
Reproducing kernel functions are obtained for the solution of generalized Kuramoto–Sivashinsky (GKS) equation in this paper. These reproducing kernel functions are valuable in the reproducing kernel Hilbert space method.
Akgül Ali   +3 more
doaj   +2 more sources

A Novel Method for Solving KdV Equation Based on Reproducing Kernel Hilbert Space Method [PDF]

open access: yesAbstract and Applied Analysis, 2013
We propose a reproducing kernel method for solving the KdV equation with initial condition based on the reproducing kernel theory. The exact solution is represented in the form of series in the reproducing kernel Hilbert space.
Mustafa Inc, Ali Akgül, Adem Kiliçman
doaj   +2 more sources

Iterative Multistep Reproducing Kernel Hilbert Space Method for Solving Strongly Nonlinear Oscillators [PDF]

open access: yesAdvances in Mathematical Physics, 2014
A new algorithm called multistep reproducing kernel Hilbert space method is represented to solve nonlinear oscillator’s models. The proposed scheme is a modification of the reproducing kernel Hilbert space method, which will increase the intervals of ...
Banan Maayah   +3 more
doaj   +2 more sources

Numerical technique for solving physical models using reproducing kernel Hilbert space method with purely integral conditions

open access: yesBoundary Value Problems
In this work, we investigate the Klein–Gordon equation, a physical problem, using the reproducing kernel Hilbert space method (RKHSM). The analytical solution is expressed as a series within the reproducing kernel Hilbert space (RKHS).
Hadjer Zerouali   +6 more
doaj   +2 more sources

Solving a Class of Singular Fifth-Order Boundary Value Problems Using Reproducing Kernel Hilbert Space Method [PDF]

open access: yesAbstract and Applied Analysis, 2013
We use the reproducing kernel Hilbert space method to solve the fifth-order boundary value problems. The exact solution to the fifth-order boundary value problems is obtained in reproducing kernel space.
Yulan Wang   +4 more
doaj   +2 more sources

Reproducing kernel functions for linear tenth-order boundary value problems

open access: yesITM Web of Conferences, 2018
Higher order differential equations have always been an onerous problem to investigate for the mathematicians and engineers. Different numerical methods were applied to get numerical approximations of such problems.
Akgül Ali   +3 more
doaj   +2 more sources

A signal theory approach to support vector classification: the sinc kernel [PDF]

open access: yes, 2009
Fourier-based regularisation is considered for the support vector machine classification problem over absolutely integrable loss functions. By invoking the modest assumption that the decision function belongs to a Paley–Wiener space, it is shown that the
Nelso, James D.B.   +3 more
core   +1 more source

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