2010
DOI: 10.1080/00207160802610843
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Solving the forced Duffing equation with integral boundary conditions in the reproducing kernel space

Abstract: In this paper, we propose a new method to solve the forced Duffing equation with integral boundary conditions. Its exact solution is represented in the form of a series in the reproducing kernel space. The n-term approximation u n (x) of the exact solution u(x) is proved to converge to the exact solution. Some numerical examples are displayed to demonstrate the accuracy of the present method.

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Cited by 34 publications
(19 citation statements)
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“…For a discussion of existence and uniqueness results and for applications of problems with integral boundary conditions, one can refer [3][4][5] and the references therein. In [2,6,7], some approximating or numerical treatment aspects of this kind of problems have been considered. However, the methods or algorithms developed so far mainly concerned with the regular cases (i.e., when the boundary layers are absent).…”
Section: Introductionmentioning
confidence: 99%
“…For a discussion of existence and uniqueness results and for applications of problems with integral boundary conditions, one can refer [3][4][5] and the references therein. In [2,6,7], some approximating or numerical treatment aspects of this kind of problems have been considered. However, the methods or algorithms developed so far mainly concerned with the regular cases (i.e., when the boundary layers are absent).…”
Section: Introductionmentioning
confidence: 99%
“…This technique gives the solution in a rapidly convergent series with components that can be easily computed. This method is used for the investigation of several scientific applications, see [20], [25], and [33]. This paper is organized as follows.…”
Section: Introductionmentioning
confidence: 99%
“…The book [6] presents an overview for the RKM. Many problems such as population models and complex dynamics have been solved in the reproducing kernel spaces [7,17,26,27]. For more details of this method see [1,2,5,8,15,16,19,25,28,29].…”
Section: Introductionmentioning
confidence: 99%