scholarly journals Stability and approximation of solutions in new reproducing kernel Hilbert spaces on a semi-infinite domain

Author(s):  
Jabar Hassan ◽  
David E. Grow

We introduce new reproducing kernel Hilbert spaces on a trapezoidal semi-infinite domain $B_{\infty}$ in the plane. We establish uniform approximation results in terms of the number of nodes on compact subsets of $B_{\infty}$ for solutions to nonhomogeneous hyperbolic partial differential equations in one of these spaces, $\widetilde{W}(B_{\infty})$. Furthermore, we demonstrate the stability of such solutions with respect to the driver. Finally, we give an example to illustrate the efficiency and accuracy of our results.

Author(s):  
Reham K. Alshehri ◽  
Banan S. Maayah ◽  
Abdelhalim Ebaid

Delay differential equations (DDEs) are generalization of the ordinary differential equation (ODEs), which is suitable for physical system that also depends on the past data. In this paper, the Reproducing Kernel Hilbert Spaces (RKHS) method is applied to approximate the solution of a general form of first, second and third order fractional DDEs (FDDEs). It is a relatively new analytical technique. The analytical and approximate solutions are represented in terms of series in the RKHS.


2013 ◽  
Vol 11 (05) ◽  
pp. 1350020 ◽  
Author(s):  
HONGWEI SUN ◽  
QIANG WU

We study the asymptotical properties of indefinite kernel network with coefficient regularization and dependent sampling. The framework under investigation is different from classical kernel learning. Positive definiteness is not required by the kernel function and the samples are allowed to be weakly dependent with the dependence measured by a strong mixing condition. By a new kernel decomposition technique introduced in [27], two reproducing kernel Hilbert spaces and their associated kernel integral operators are used to characterize the properties and learnability of the hypothesis function class. Capacity independent error bounds and learning rates are deduced.


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