Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 158134, 10 pages

http://dx.doi.org/10.1155/2015/158134

## New Reproducing Kernel Functions

Department of Mathematics, Faculty of Education, Dicle University, 21280 Diyarbakır, Turkey

Received 18 November 2014; Revised 10 January 2015; Accepted 21 January 2015

Academic Editor: Masoud Hajarian

Copyright © 2015 Ali Akgül. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Some new reproducing kernel functions on time scales are presented. Reproducing kernel functions have not been found on time scales till now. These functions are very important on time scales and they will be very useful for researchers. We need these functions to solve dynamic equations on time scales with the reproducing kernel method.

#### 1. Introduction

A time scale (which is a special case of a measure chain) is an arbitrary nonempty closed subset of the real numbers. Thus, that is, the real numbers, the integers, the natural numbers, and the nonnegative integers are examples of time scales, as are while that is, the rational numbers, the irrational numbers, the complex numbers, and the open interval between and , are not time scales. The theory of time scales was presented by Stefan Hilger in order to unify continuous and discrete analysis [1]. For more details of time scales see [2].

A time scale is denoted by in this work. The delta derivative for a function defined on is given as [1] follows:(i) is the usual derivative if ;(ii) is the usual forward difference operator if .

Reproducing kernel functions on time scale have been found in this work. These new reproducing kernel functions correspond to old reproducing kernel functions when .

The paper is organized as follows. The history of the reproducing kernel method (RKM) is given in Section 2. Section 3 introduces several reproducing kernel spaces for differential equations. Section 4 is devoted to the reproducing kernel functions on time scales. Derivation of the reproducing kernel Hilbert space on time scales is presented in Section 5. It has been proved that the new reproducing kernel functions on time scales coincide with the old reproducing kernel functions when in Section 6. Examples are illustrated in Section 7. There are some conclusions in Section 8.

#### 2. History of Reproducing Kernel Method

In this section, the history of RKM will be investigated. Reproducing kernel space is a special Hilbert space. In recent years, there are many papers on the solution of the nonlinear problems with RKM [3]. The concept of the reproducing kernel can be traced back to the paper of Zaremba [4] in 1908. It was proposed for discussing the boundary value problems of the harmonic functions. In the early development stage of the reproducing kernel theory, most of the works were applied by Bergman [5]. Bergman asserted the corresponding kernels of the harmonic functions with one or several variables and the corresponding kernel of the analytic function in squared metric and applied them in the research of the boundary value problem of the elliptic partial differential equation. This is the first stage in the development history of the reproducing kernel [3].

The second development stage of the reproducing kernel theory was started by Mercer [6]. Mercer discovered that the continuous kernel of the positive definite integral equation has the positive definite property [3] He named the kernel with this property as positive definite Hermite matrix. He also found out that the positive defined Hermite matrix corresponded to a function family, proposed a Hilbert space with inner product , and proved the reproducibility of the kernel in this space:

The third development stage of the reproducing kernel theory is related to Aronszajn [7]. In 1950, he reduced the works of the formers and studied a systematic reproducing kernel theory including the Bergman kernel function.

RKM, which accurately computes the series solution, is of great interest to applied sciences. Recently, a lot of research work has been devoted to the application of RKM to a wide class of stochastic and deterministic problems involving fractional differential equation, nonlinear oscillator with discontinuity, singular nonlinear two-point periodic boundary value problems, integral equations, and nonlinear partial differential equations [3]. The efficiency of RKM has been used by many authors to investigate several scientific applications. Geng and Cui [8] and Zhou et al. [9] applied RKM to handle second-order boundary value problems. Yao and Lin [10] and Wang et al. [11] investigated a class of singular boundary value problems by RKM. In [12], RKM was used to solve nonlinear infinite-delay-differential equations. Wang and Chao [13] and Zhou et al. [9] independently employed RKM to variable-coefficient partial differential equations. Geng and Cui [14] and Du and Cui [15] investigated the approximate solution of the forced Duffing equation with integral boundary conditions by combining the homotopy perturbation method and RKM. Lv and Cui [16] presented a new algorithm to solve linear fifth-order boundary value problems. In [17], the authors developed a new existence proof of solutions for nonlinear boundary value problems. Cui and Du [18] obtained the representation of the exact solution for nonlinear Volterra-Fredholm integral equations by using RKM. Wu and Li [19] applied an iterative reproducing kernel method to obtain the analytical approximate solution of a nonlinear oscillator with discontinuities. Recently, RKM was applied to fractional partial differential equations and multipoint boundary value problems [17]. For more details about RKM and the modified forms and its effectiveness, see [20] and the references therein.

#### 3. Reproducing Kernel Functions for Differential Equations

In this section, we define some useful reproducing kernel functions for differential equations.

*Definition 1 (reproducing kernel function). *Let . A function is called a* reproducing kernel function* of the Hilbert space if and only if (a) for all ,(b) for all and all .

The last condition is called “the reproducing property” as the value of the function at the point is reproduced by the inner product of with .

*Definition 2 (reproducing kernel Hilbert space). *A Hilbert space which is defined on a nonempty set is called a* reproducing kernel Hilbert space* if there exists a reproducing kernel function .

*Definition 3. *We define the space by
where denotes the space of absolutely continuous functions. The inner product and the norm in are defined by

Theorem 4. *The space is a reproducing kernel space, and its reproducing kernel function is given by
*

*Proof. *By Definition 3, we have
Integrating this equation by parts one time, we get
Note that property of the reproducing kernel is
If
then (9) gives
When , we have
Therefore,
Since
we get
The unknown coefficients and can be obtained by (12)–(18). Thus, is acquired as

*Definition 5. *We define the space by
The inner product and the norm in are defined by

*Theorem 6. The space is a reproducing kernel space, and its reproducing kernel function is given by*

*Proof. *By Definition 5, we have
Integrating (23) by parts two times, we get
Note that property of the reproducing kernel is
If
then (23) gives
When , we get
Thus,
Since
we have
The unknown coefficients and can be obtained by (26)–(32). Thus, is achieved as

*Definition 7. *We define the space by
The inner product and the norm in are defined by

*Theorem 8. The space is a reproducing kernel space, and its reproducing kernel function is given by
*

*Proof. *By Definition 7, we get
Integrating (37) by parts three times, we obtain
Note that property of the reproducing kernel is

If
then, (37) gives
When , we know
Consequently, we attain
Since
we have
The unknown coefficients and can be obtained by (40)–(46). Thus, is gained as

*4. Reproducing Kernel Functions on Time Scales*

*4. Reproducing Kernel Functions on Time Scales**In this section, we define some useful reproducing kernel functions on time scale . All functions are new in the literature.*

*Definition 9 (see [1, page 22]). *A function is called regulated provided its right-sided limits exist (finite) at all right-dense points in and its left-sided limits exist (finite) at all left-dense points in .

*Definition 10 (see [1, page 22]). *A function is called rd-continuous provided it is continuous at right-dense points in and it is left-sided limits exist (finite) at left-dense points in . The set of rd-continuous functions will be denoted by .

*Lemma 11 (see [1, page 28]). If , and , then (i);(ii);(iii);(iv);(v);(vi);(vii);(viii)if on , then
(ix)if for all , then .*

*Definition 12. *We define the space as the set of all functions defined on . The inner product and the norm in are defined by

*Theorem 13. The space is a reproducing kernel space, and its reproducing kernel function is given by
*

*Proof. *Define by (50) and note that
Let and let . Then, by Definition 12, we have
This completes the proof.

*Definition 14. *The inner product and the norm in are defined by

*Theorem 15. The space is a reproducing kernel space, and its reproducing kernel function is given by
*

*Proof. *Define by (54) and note that
By Definition 14 and integration by parts, we have
This completes the proof.

*Definition 16. *The inner product and the norm in are defined by

*Theorem 17. The space is a reproducing kernel space, and its reproducing kernel function is given by
*

*Proof. *Define by (58) and note that

By Definition 16, we have
This completes the proof.

*5. Derivation of Reproducing Kernel Hilbert Space on Time Scales*

*5. Derivation of Reproducing Kernel Hilbert Space on Time Scales**Derivation of the new reproducing kernel functions are presented in this section. By Definition 9, we have
We get
by integration by parts. We have
We acquire Table 1 by (63). We get
by Table 1, and can be found in a similar way.*