Advances in Fuzzy Systems

Volume 2018, Article ID 5405124, 8 pages

https://doi.org/10.1155/2018/5405124

## Numerical Solution of Two-Dimensional Linear Fuzzy Fredholm Integral Equations by the Fuzzy Lagrange Interpolation

Department of Mathematics, Karaj Branch, Islamic Azad University, Karaj, Iran

Correspondence should be addressed to R. Ezzati; ri.ca.uaik@itaze

Received 26 October 2017; Accepted 19 May 2018; Published 2 December 2018

Academic Editor: Oscar Castillo

Copyright © 2018 H. Nouriani and R. Ezzati. 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

In this study, at first, we propose a new approach based on the two-dimensional fuzzy Lagrange interpolation and iterative method to approximate the solution of two-dimensional linear fuzzy Fredholm integral equation (2DLFFIE). Then, we prove convergence analysis and numerical stability analysis for the proposed numerical algorithm by two theorems. Finally, by some examples, we show the efficiency of the proposed method.

#### 1. Introduction

Recently many authors proposed various numerical methods for solving one-dimensional fuzzy integral equations [1–9]. Also, two-dimensional fuzzy integral equations have been noticed by a lot of researchers because of their broad applications in engineering sciences. Some of the most important papers in this area are trapezoidal quadrature rule and iterative method [10–12], triangular functions [13], quadrature iterative [14], Bernstein polynomials [15], collocation fuzzy wavelet like operator [16], homotopy analysis method (HAM) [17], open fuzzy cubature rule [18], kernel iterative method [19], modified homotopy pertubation [20], block-pulse functions [21], optimal fuzzy quadrature formula [22], and finally, iterative method and fuzzy bivariate block-pulse functions [23]. Also, some researchers have solved one-dimensional fuzzy Fredholm integral equations by using fuzzy interpolation via iterative method such as: iterative interpolation method [9], Lagrange interpolation based on the extension principle [5], and spline interpolation [7].

As we know interpolation is one of the most substantial and the most applicable methods in numerical analysis. So, in this paper, we want to solve 2DLFFIEs by applying two-dimensional fuzzy Lagrange interpolation and iterative method. First of all an approximate solution of integral by applying Lagrange interpolation and iterative method is provided. Then, convergence analysis and numerical stability analysis of the proposed method in Theorems 11 and 12 are proved.

The paper is organized as follows: Some notations and theorems about the structure of fuzzy sets are reviewed in Section 2. In Section 3, firstly, we introduce two-dimensional fuzzy Lagrange interpolation. Also, we propose two-dimensional fuzzy Lagrange interpolation and iterative method for solving 2DLFFIEs. In Section 4, we verify convergence analysis for proposed method. Also, in Section 5, we prove numerical stability analysis for the method. Two numerical examples are presented in Section 6.

#### 2. Preliminaries

At first, we review some basic definitions and necessary results about fuzzy set theory.

*Definition 1 (see [24]). *A fuzzy number is a function with the following properties: (1) such that .(2).(3) and , neighborhood .(4)In , the set is compact. The set of all fuzzy numbers is denoted by .

*Definition 2 (see [24]). *For and , define and Then it is well known that, for each is a bounded and closed interval of . We define uniquely the sum and the product for , and by where means the usual addition of two intervals (as subsets of ) and means the usual product between a scalar and a subset of . Notice and it holds , . If then . Actually , where , , , . For one has , respectively.

*Definition 3 (see [24]). *Define by where ; , . Clearly is a metric on . Also is a complete metric space, with the following properties [24]:

*Definition 4 (see [24]). *Suppose be fuzzy number valued functions, then the distance between and is defined by

Lemma 5 (see [24]). *(1) If we denote , then , .(2)With respect to , none of , has opposite in .(3)Let , , and any , we have . Notice that, for general , , the above property is false.(4)For any and any , , we have .(5)For any , and any , we have . If we denote , , then has the properties of a usual norm on , i.e., Notice that is not a linear space over , and consequently is not a normed space. Here denotes the fuzzy summation.*

*Definition 6 (see [24]). *A fuzzy valued function is said to be continuous at , if for each there exists such that , whenever and . We say that is fuzzy continuous on if is continuous at each and denotes the space of all such functions by .

*Definition 7 (see [11]). *Suppose that is a bounded mapping. The function defined by is called modules of oscillation of on . Also, if , then is called uniform modules of continuity of .

*Theorem 8 (see [11]). The following properties hold: (1), , ;(2) is a nondecreasing mapping in ;(3);(4), ;(5), , ;(6), .*

*Theorem 9 (see [11]). If and are Henstock integrable mapping on and if is Lebesgue integrable, then *

*3. The Main Result*

*3. The Main Result*

*In this section, first, we introduce two-dimensional fuzzy Lagrange interpolation. Then, we propose two-dimensional fuzzy Lagrange interpolation and iterative method for solving (9).*

*Consider 2DLFFIE as follows:where , is an arbitrary positive function on and . We assume that is continuous, and therefore it is uniformly continuous with respect to . So, there exists such that .*

*Two-dimensional Lagrange polynomials, , are defined as follows:where is the Lagrange polynomial and is defined as follows:therefore, So, the two-dimensional interpolation in the Lagrange form is (see [25])where the coefficients are the fuzzy numbers.*

*Here, we consider the two-dimensional fuzzy Lagrange interpolation in the given points and such thatNow, we propose a numerical method to solve (9). To do this, we suppose the following iterative procedure to approximate the solution of (9) in point whereIn Theorem 10, authors of [11] proved the existence and uniqueness solution of (9) by using the Banach fixed point theorem.*

*Theorem 10 (see [11]). Let the function be continuous and positive for , and , and let be continuous on . If then the fuzzy integral equation (9) has a unique solution , where be the space of two-dimensional fuzzy continuous functions with the metric and it can be obtained by the following successive approximations method:Moreover, the sequence of successive approximations converges to the solution . Furthermore, the following error bound holds:where .*

*4. Convergence Analysis*

*4. Convergence Analysis*

*In this section, we obtain an error estimate between the exact solution and the approximate solution of 2DLFFIE (9).*

*Theorem 11. Under the hypotheses of Theorem 10 and , the iterative procedure (15) converges to the unique solution of (9), , and its error estimate is as follows: *

*Proof. *Clearly, we have From (18) and (15), we conclude that By supposing , we get Also, we have Therefore, where , . Hence, we conclude that So, If , then we obtain therefore

*5. Numerical Stability Analysis*

*5. Numerical Stability Analysis*

*To show the numerical stability analysis of the proposed method in previous section, we consider another starting approximation such that for which . The obtained sequence of successive approximations is and using the same iterative method, the terms of produced sequence are *

*Theorem 12. The proposed method (15), under the assumptions of Theorem 11, is numerically stable with respect to the choice of the first iteration.*

*Proof. *At first, we obtain that However, We conclude thatand thus So, Therefore,

*6. Numerical Examples*

*6. Numerical Examples*

*In this section, we use the proposed method in two-dimensional linear fuzzy Fredholm integral equations for solving two examples. By using the proposed method for , , and in , we present the absolute errors in Tables 1–4.*