Abstract
There has recently been considerable focus on finding reliable and more effective numerical methods for solving different mathematical problems with integral equations. The Runge–Kutta methods in numerical analysis are a family of iterative methods, both implicit and explicit, with different orders of accuracy, used in temporal and modification for the numerical solutions of integral equations. Fuzzy Integral equations (known as FIEs) make extensive use of many scientific analysis and engineering applications. They appear because of the incomplete information from their mathematical models and their parameters under fuzzy domain. In this paper, the sixth order Runge-Kutta is used to solve second-kind fuzzy Volterra integral equations numerically. The proposed method is reformulated and updated for solving fuzzy second-kind Volterra integral equations in general form by using properties and descriptions of fuzzy set theory. Furthermore a Volterra fuzzy integral equation, based on the parametric form of a fuzzy numbers, transforms into two integral equations of the second kind in the crisp case under fuzzy properties. We apply our modified method using the specific example with a linear fuzzy integral Volterra equation to illustrate the strengths and accurateness of this process. A comparison of evaluated numerical results with the exact solution for each fuzzy level set is displayed in the form of table and figures. Such results indicate that the proposed approach is remarkably feasible and easy to use.
Highlights
Integral equations discover specific pertinence in numerous logical and numerical mathematical models
One of the significant components of a fuzzy analytical theory that plays a key role in numerical analysis is fuzzy integral equations
The theory has developed since, and is a separate branch of applied mathematics and used to solve many mathematical problems in the filed on differential equations [2,3]
Summary
Integral equations discover specific pertinence in numerous logical and numerical mathematical models. One of the significant components of a fuzzy analytical theory that plays a key role in numerical analysis is fuzzy integral equations. Friedman et al investigated that core of numerical approaches to solving fuzzy integral equations with arbitrary kernels [14]. It would be important to analyze the RKM6 which was not used to solve FVIE2. We present the use fuzzy set theory properties to formulate RKM6 for solving and analyze numerical solution FVIE2 involving linear test example in the form of table and figures.
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