Results 21 to 30 of about 2,360 (218)
Reproducing Kernel Method for Solving Nonlinear Differential-Difference Equations [PDF]
Abstract and Applied Analysis, 2012 On the basis of reproducing kernel Hilbert spaces theory, an iterative algorithm for solving some nonlinear differential-difference equations (NDDEs) is presented.
Reza Mokhtari +2 more
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Reproducing kernel method for solving wiener-hopf equations of the second kind [PDF]
Journal of Hyperstructures, 2016 This paper proposed a reproducing kernel method for solving Wiener-Hopf equations of the second kind. In order to eliminate the singularity of the equation, a transform is used.
Azizallah Alvandi +2 more
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A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice. [PDF]
Plant GenomeAbstract Rice (Oryza sativa) is an important staple food, feeding more than half of the global population. A feasible improvement of rice yield is necessary to meet the ever–growing food demands. Genomic selection (GS), as an advanced breeding technique, enables the prediction of phenotypes solely based on genotypic data using a constructed genomic ...
Hu X +8 more
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Mathematical Modelling and Analysis, 2021 The main aim of this paper is to present a hybrid scheme of both meshless Galerkin and reproducing kernel Hilbert space methods. The Galerkin meshless method is a powerful tool for solving a large class of multi-dimension problems.
Mehdi Mesrizadeh, Kamal Shanazari
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A method for approximate missing data from data error measured with l norm [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2021 We briefly review some recent work on hypercircle inequality for partially corrupted data when the data error is measured with l norm. The aim of this paper is to present the method for approximate missing data in the use of midpoint algorithm and
Benjawan Rodjanadid, Kannika Khompungson
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Reproducing Kernel Hilbert Spaces Regression and Classification Methods [PDF]
, 2022 AbstractThe fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning.
Osval Antonio Montesinos López +2 more
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A novel method for fractal-fractional differential equations
Alexandria Engineering Journal, 2022 We consider the reproducing kernel Hilbert space method to construct numerical solutions for some basic fractional ordinary differential equations (FODEs) under fractal fractional derivative with the generalized Mittag–Leffler (M-L) kernel.
Nourhane Attia +4 more
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A reproducing kernel Hilbert space approach in meshless collocation method [PDF]
Computational and Applied Mathematics, 2019 In this paper we combine the theory of reproducing kernel Hilbert spaces with the field of collocation methods to solve boundary value problems with special emphasis on reproducing property of kernels. From the reproducing property of kernels we proposed a new efficient algorithm to obtain the cardinal functions of a reproducing kernel Hilbert space ...
Babak Azarnavid +3 more
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Aveiro method in reproducing kernel Hilbert spaces under complete dictionary [PDF]
Mathematical Methods in the Applied Sciences, 2017 Aveiro method is a sparse representation method in reproducing kernel Hilbert spaces, which gives orthogonal projections in linear combinations of reproducing kernels over uniqueness sets. It, however, suffers from determination of uniqueness sets in the underlying reproducing kernel Hilbert space.
Weixiong Mai, Tao Qian
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Reproducing kernel Hilbert space method for solving fractal fractional differential equations
Results in Physics, 2022 Based on reproducing kernel theory, an analytical approach is considered to construct numerical solutions for some basic fractional ordinary differential equations (FODEs, for short) under fractal fractional derivative with the exponential decay kernel ...
Nourhane Attia +4 more
doaj +1 more source