Based on reproducing kernel theory, an effective numerical technique is proposed for solving second order linear two-point boundary value problems with deviating argument. In this method, reproducing kernels with Chebyshev polynomial form are used (C-RKM). The convergence and an error estimation of the method are discussed. The efficiency and the accuracy of the method is demonstrated on some n...

We present a new class of continuously defined parametric snakes using a special kind of exponential splines as basis functions. We have enforced our bases to have the shortestpossible support subject to some design constraints to maximize efficiency. While the resulting snakes are versatile enough to provide a good approximation of any closed curve in the plane, their most important feature is...

We first introduce some related definitions of the bounded linear operator L in the reproducing kernel space W(2)(m)(D). Then we show spectral analysis of L and derive several property theorems.

on the basis of a reproducing kernel space, an iterative algorithm for solving the one-dimensional linear and nonlinear schrödinger equations is presented. the analytical solution is shown in a series form in the reproducing kernel space and the approximate solution is constructed by truncating the series. the convergence of the approximate solution to the analytical solution is also proved. th...

Expectile regression is a useful alternative to conditional mean and quantile for characterizing response distribution, especially when the distribution asymmetric or its tails are of interest. In this article, we propose class scalar‐on‐function linear expectile models where functional slope parameter assumed reside in reproducing kernel Hilbert space (RKHS). Our approach addresses numerous dr...