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Contents lists available atScienceDirect

Computers and Mathematics with Applications

journal homepage:www.elsevier.com/locate/camwa

An analytical method for solving linear Fredholm fuzzy integral

equations of the second kind

A. Molabahrami

a,b,∗

, A. Shidfar

b

, A. Ghyasi

a aDepartment of Mathematics, Ilam University, PO Box 69315516, Ilam, Iran bDepartment of Mathematics, Iran University of Science and Technology, Tehran, Iran

a r t i c l e i n f o

Article history: Received 15 June 2010

Received in revised form 30 January 2011 Accepted 7 March 2011

Keywords: Fuzzy number Fuzzy function

Fuzzy Fredholm integral equation Homotopy analysis method Series solution

a b s t r a c t

In this paper, we use the parametric form of a fuzzy number and convert a linear fuzzy Fredholm integral equation to two linear systems of integral equations of the second kind in the crisp case. For fuzzy Fredholm integral equations with kernels, the sign of which is difficult to determine, a new parametric form of the fuzzy Fredholm integral equation is introduced. We use the homotopy analysis method to find the approximate solution of the system, and hence, obtain an approximation for fuzzy solutions of the linear fuzzy Fredholm integral equation of the second kind. The proposed method is illustrated by solving some examples. Using the HAM, it is possible to find the exact solution or the approximate solution of the problem in the form of a series.

© 2011 Elsevier Ltd. All rights reserved. 1. Introduction

The concept of integration of fuzzy functions was first introduced by Dubois and Prade [1] and investigated by Goetschel and Voxman [2], Kaleva [3], Matloka [4] and others. Congxin and Ming [5] presented the first applications of fuzzy integration. They investigated the fuzzy Fredholm integral equation of the second kind (FFIE-2). One of the first applications of fuzzy integration was given by Wu and Ma [6] who investigated the fuzzy Fredholm integral equation of the second kind (FFIE-2). This work, which established the existence of a unique solution to FFIE-2, was followed by other work on FIE [7], where a fuzzy integral equation replaced an original fuzzy differential equation.

Recently, Babolian et al. [8] has used the Adomian decomposition method (ADM) to solve linear Fredholm fuzzy integral equations of the second kind.

First, Liao in 1992 employed basic ideas of homotopy in topology to propose a general analytic method for nonlinear problems, namely, the homotopy analysis method (HAM) [9,10]. After this, the the homotopy analysis method has been applied to obtain formal solutions to a wide class of deterministic direct and inverse problems [11–16]. The purpose of this paper is to extend the application of the HAM for solving the fuzzy Fredholm integral equation of the second kind (FFIE-2). In this paper, the basic idea of the HAM is introduced and then the application of the HAM for the fuzzy Fredholm integral equation of the second kind (FFIE-2) is extended. For kernels, the sign of which is difficult to determine, we introduced a new parametric form of FFIE. We shall apply HAM to find the approximate analytical solutions of some fuzzy Fredholm integral equations of the second kind (FFIEs-2).

2. Preliminaries

In this section, the most basic notations used in fuzzy calculus are introduced [8,17].

Corresponding author at: Department of Mathematics, Ilam University, PO Box 69315516, Ilam, Iran. Tel.: +98 914 143 6187; fax: +98 21 7724 0472. E-mail addresses:a_m_bahrami@iust.ac.ir(A. Molabahrami),shidfar@iust.ac.ir(A. Shidfar),azarghyasi@yahoo.com(A. Ghyasi).

0898-1221/$ – see front matter©2011 Elsevier Ltd. All rights reserved.

(2)

Definition 1. A fuzzy number is a fuzzy set u

:

R1

→ [

0

,

1

]

which satisfies i. u is upper semicontinuous,

ii. u

(

x

) =

0 outside some interval

[

c

,

d

]

, and

iii. There are real numbers a and b

,

c

a

b

d, for which

u

(

x

)

is monotonic increasing on

[

c

,

a

]

,

u

(

x

)

is monotonic decreasing on

[

b

,

d

]

, and

u

(

x

) =

1 for a

x

b.

The set of all fuzzy numbers, as given byDefinition 1, is denoted by E1. An alternative definition or parametric form of a fuzzy number which yields the same E1is given by Kaleva [3].

Definition 2. A fuzzy number u is a pair

(

u

,

u

)

of functions u

(

r

)

and u

(

r

),

0

r

1, satisfying the following requirements i. u

(

r

)

is a bounded monotonic increasing left continuous function,

ii. u

(

r

)

is a bounded monotonic decreasing left continuous function, and iii. u

(

r

) ≤

u

(

r

), ≤

r

1.

For arbitrary u

=

(

u

,

u

), v = (v, v)

and k

>

0 we define addition

(

u

+

v)

and multiplication by k as

(

u

+

v)(

r

) =

u

(

r

) + v(

r

),

(

u

+

v)(

r

) =

u

(

r

) + v(

r

),

(1)

(

ku

)(

r

) =

ku

(

r

),

(

ku

)(

r

) =

ku

(

r

) + v(

r

).

(2)

The collection of all the fuzzy numbers with addition and multiplication as defined by Eqs.(1)and(2)is denoted by E1and is a convex cone. It can be shown that Eqs.(1)and(2)are equivalent to the addition and multiplication as defined by using the

α

-cut approach [2] and the extension principles [18]. We will next define the fuzzy function notation and a metric D in

E1[2].

Definition 3. For arbitrary fuzzy numbers u

=

(

u

,

u

)

and

v = (v, v)

the quantity

D

(

u

, v) =

max

sup or≤1

|

u

(

r

) − v(

r

)| ,

sup or≤1

u

(

r

) − v(

r

)

,

(3)

is the distance between u and

v

[19].

This metric is equivalent to the one used by Puri and Ralescu [20], and Kaleva [3]. It is shown [21] that

(

E1

,

D

)

is a complete metric space. Goetschel and Voxman [2] defined the integral of a fuzzy function using the Riemann integral concept. If the fuzzy function f

(

t

)

is continuous in the metric D, its definite integral exists [2]. Furthermore,

b a f

(

t

,

r

)

dt

=

b a f

(

t

,

r

)

dt

,

b a f

(

t

,

r

)

dt

=

b a f

(

t

,

r

)

dt

.

(4)

3. Fuzzy integral equation

The Fredholm integral equation of the second kind is [8,22]

F

(

t

) =

f

(

t

) + λ

b a

K

(

s

,

t

)

F

(

s

)

ds

,

(5)

where

λ >

0

,

K

(

s

,

t

)

is an arbitrary kernel function over the square a

s

,

t

b and f

(

t

)

is a function of t

,

a

t

b.

If f

(

t

)

is a crisp function then the solutions of Eq.(5)are crisp as well. However, if f

(

t

)

is a fuzzy function these equations may only possess fuzzy solutions. Sufficient conditions for the existence of a unique solution to the fuzzy Fredholm integral equation of the second kind, i.e. to Eq.(5)where f

(

t

)

is a fuzzy function, are given in [6].

Now, we introduce the parametric form of an FFIE-2 with respect to Definition 2. Let

(

f

(

t

,

r

),

f

(

t

,

r

))

and

(

u

(

t

,

r

),

u

(

t

,

r

)),

0

r

1 and t

∈ [

a

,

b

]

, be parametric forms of f

(

t

)

and u

(

t

)

, respectively; then the parametric form of FFIE-2 is as follows [17]

(3)

u

(

t

,

r

) =

f

(

t

,

r

) + λ

b a

v

1

(

s

,

t

,

u

(

s

,

r

),

u

(

s

,

r

))

ds

,

u

(

t

,

r

) =

f

(

t

,

r

) + λ

b a

v

2

(

s

,

t

,

u

(

s

,

r

),

u

(

s

,

r

))

ds

,

(6) where

v

1

(

s

,

t

,

u

(

s

,

r

),

u

(

s

,

r

)) =

K

(

s

,

t

)

u

(

s

,

r

),

K

(

s

,

t

) ≥

0

,

K

(

s

,

t

)

u

(

s

,

r

),

K

(

s

,

t

) <

0

,

and

v

2

(

s

,

t

,

u

(

s

,

r

),

u

(

s

,

r

)) =

K

(

s

,

t

)

u

(

s

,

r

),

K

(

s

,

t

) ≥

0

,

K

(

s

,

t

)

u

(

s

,

r

),

K

(

s

,

t

) <

0

,

for each 0

r

1 and t

∈ [

a

,

b

]

. We can see that(6)is a system of linear Fredholm integral equations in the crisp case for each 0

r

1 and t

∈ [

a

,

b

]

. In the next section, we define the homotopy analysis method as an analytical algorithm for approximating the solution of this system of linear integral equations in the crisp case. Then we find the approximate solutions for u

(

t

,

r

)

and u

(

t

,

r

)

for each 0

r

1 and t

∈ [

a

,

b

]

.

The following theorem provides sufficient conditions for the existence of a unique solution to Eq.(5)where f

(

x

)

is a fuzzy function, and the rate of convergence of error.

Theorem 1. Let K

(

s

,

t

)

be continuous for a

s

,

t

b

, λ >

0 and f

(

t

)

a fuzzy continuous function of t

,

a

t

b. If

λ <

1

M

(

b

a

)

,

where M

=

Maxas,tb

|

K

(

s

,

t

)|

, then the iterative procedure

F0

(

t

) =

f

(

t

),

Fk

(

t

) =

f

(

t

) + λ

b a

K

(

s

,

t

)

Fk−1

(

s

)

ds

,

k

1

,

converges to the unique solution of Eq.(5). Specifically,

sup atb D

(

F

(

t

),

Fk

(

t

)) ≤

Lk 1

Lasup≤tb D

(

F

(

t

),

F1

(

t

)) ,

where L

=

λ

M

(

b

a

)

. This infers that Fk

(

t

)

converges uniformly in t to F

(

t

)

, i.e. given arbitrary

ϵ >

0 we can find N such that

D

(

F

(

t

),

Fk

(

t

)) < ϵ,

a

t

b

,

k

>

N

.

Proof. See [23]. 

4. HAM solution for linear system of Fredholm integral equations Consider the system of linear Fredholm integral equations of the form [8]

U

(

t

) =

F

(

t

) +

b a K

(

s

,

t

)

U

(

s

)

ds

,

(7) where U

(

t

) = (

u1

(

t

), . . . ,

un

(

t

))

T

,

F

(

t

) = (

f1

(

t

), . . . ,

fn

(

t

))

T

,

K

(

s

,

t

) = 

kij

(

s

,

t

) ,

i

=

1

,

2

, . . . ,

n

,

j

=

1

,

2

, . . . ,

n

.

For the purpose, we first give the following definition.

Definition 4. Let

φ

be a function of the homotopy-parameter q, then

Dm

(φ) =

1 m

!

m

φ

qm

q=0

,

(8)

(4)

From Eq.(7), the nonlinear operator is defined as follows

N

(

t

;

q

) =

U

(

t

;

q

) −

F

(

t

) −

b a

K

(

s

,

t

)

U

(

s

;

q

)

ds

,

(9)

and we choose the auxiliary linear operator as follows

L

[

Φ

(

t

;

q

)] =

Φ

(

t

;

q

).

(10)

We consider the so-called zero-order deformation equation

(

1

q

)

L

[

Φ

(

t

;

q

) −

Ψ0

(

t

)] =

qhH

¯

(

t

)

N

[

Φ

(

t

;

q

)],

(11) where q

∈ [

0

,

1

]

is the embedding parameter,h is a diagonal matrix of nonzero convergence-parameters, H

¯

(

t

)

is a diagonal matrix of auxiliary functions,Ψ0

(

t

)

is an initial guess of the exact solutionΨ

(

t

)

andΦ

(

t

;

q

)

is an unknown function which depends also on convergence-parameters and auxiliary functions. ExpandingΦ

(

t

;

q

)

in Taylor series with respect to q, we have Φ

(

t

;

q

) =

Ψ0

(

t

) +

+∞

m=1 Ψm

(

t

)

qm

,

(12) where Ψm

(

t

) =

Dm

(

t

;

q

)

]

.

Operating on both sides of Eq.(11)with Dm, we have the so-called mth-order deformation equation

L

[

Ψm

(

t

) − χ

mΨm−1

(

t

)] = ¯

hH

(

t

)

Rm

( ⃗

Ψm−1

,

t

),

(13) where Rm

( ⃗

Ψm−1

,

t

) =

Dm−1

(

N

[

Φ

(

t

;

q

)]) ,

(14) and

χ

m

=

0

,

m⩽1

,

1

,

m⩾2

.

For Eqs.(9)and(14), we have

Rm

[ ⃗

Ψm−1

(

t

)] =

Ψm−1

(

t

) −

b a

K

(

s

,

t

)

Ψm−1

(

s

)

ds

(

1

χ

m

)

F

(

t

).

(15)

5. Test examples

To show the efficiency of the HAM described in the previous section, we present some examples. For all examples, we choose H

(

t

) =

I, u0

(

t

,

r

) =

f

(

t

,

r

)

and u0

(

t

,

r

) =

f

(

t

,

r

)

. We use n

+

1 terms in evaluating the approximate solution

uapprox[n]

(

t

,

r

; ¯

h

) = ∑

mn=0um

(

t

,

r

; ¯

h

)

.

Example 1. Consider the fuzzy Fredholm integral equation with [8,17]

f

(

t

,

r

) =

1 15

13

(

r2

+

r

) +

2

(

4

r3

r

)

sin

t 2

,

f

(

t

,

r

) =

1 15

2

(

r2

+

r

) +

13

(

4

r3

r

)

sin

t 2

,

and K

(

s

,

t

) =

0

.

1 sin

(

s

)

sin

(

t

/

2

) ,

0

s

,

t

2

π,

and a

=

0

,

b

=

2

π

. The exact solution in this case is given by

u

(

t

,

r

) = 

r2

+

r

sin

(

t

/

2

) ,

u

(

t

,

r

) = 

4

r3

r

sin

(

t

/

2

) .

Results are shown inFigs. 1–4.

Example 2. Consider the following fuzzy Fredholm integral equation with [8]

f

(

t

,

r

) =

rt

+

3 26

3 26r

1 13t 2

1 13t 2r

,

f

(

t

,

r

) =

2t

rt

3 26

+

3 26r

3 13t 2

+

1 13t 2r

,

(5)

Fig. 1. Theh-curves of 10th-order of approximation solution given by HAM for the¯ Example 1.

Fig. 2. Comparison between the exact solution and the approximate solution given by HAM (uapprox[10](t,r; −1.5)) by metric D forExample 1. and

K

(

s

,

t

) =

s

2

+

t2

2

13

,

0

s

,

t

2

,

and a

=

0

,

b

=

2. The exact solution in this case is given by

u

(

t

,

r

) =

rt

,

u

(

t

,

r

) = (

2

r

)

t

.

Here, we set

K

(

s

,

t

) =

K+

(

s

,

t

) −

K

(

s

,

t

),

(16)

where K+

(

s

,

t

) =

Max

{

K

(

s

,

t

),

0

}

and K

(

s

,

t

) =

Max

{−

K

(

s

,

t

),

0

}

. Results are shown inFigs. 5–7. 6. Comparison and discussion

Figs. 1and5show the convergent regions of the solution series given by HAM forExamples 1 and2respectively. Comparison between the exact solution and the approximate solution given by HAM by metric D forExample 1is given byFig. 2and same comparison for HPM and ADM solutions is given byFig. 3. It is clear that the approximate solution given by HAM is more accurate than the approximate solutions given by HPM and ADM. In fact, ForExample 1, For HAM solution, we have Max

{

D

(

uapprox[10]

(

t

,

r

; −

1

.

5

),

u

(

t

,

r

))} =

4

.

708109383556773

×

10−9and for HPM and ADM solutions we obtain Max

{

D

(

uapprox[10]

(

t

,

r

; −

1

),

u

(

t

,

r

))} =

9

.

698086438946183

×

10−7.Fig. 4shows the comparison between the exact solution and the approximate solution given by HAM forExample 1at t

=

π

.

ForExample 2, by using(16)we have

v

1

(

s

,

t

,

u

(

s

,

r

), ¯

u

(

s

,

r

)) =

K+

(

s

,

t

)

u

(

s

,

r

) −

K

(

s

,

t

)

u

(

s

,

r

),

(6)

Fig. 3. Comparison between the exact solution and the approximate solution given by HPM and ADM (uapprox[10](t,r; −1)) by metric D forExample 1.

Fig. 4. Comparison between the exact solution and the approximate solution given by HAM (uapprox[10](t,r; −1.5)) forExample 1at t=π.

Fig. 5. Theh-curves of 5th-order of approximation solution given by HAM for the¯ Example 2. Thus, the new parametric form of FFIE-2 is

u

(

t

,

r

) =

f

(

t

,

r

) + λ

b a

{

K+

(

s

,

t

)

u

(

s

,

r

) −

K

(

s

,

t

)

u

(

s

,

r

)}

ds

,

u

(

t

,

r

) =

f

(

t

,

r

) + λ

b a

{

K+

(

s

,

t

)

u

(

s

,

r

) −

K

(

s

,

t

)

u

(

s

,

r

)}

ds

.

(17)

The parametric form of FFIE denoted by(17)provides us with a new and simple way to use analytical and numerical methods for solving FFIEs with kernels, the sign of which is difficult to determine.Figs. 6and7show the comparison between the exact solution and the approximate solution given by HAM forExample 2.

(7)

Fig. 6. Comparison between the exact solution and the approximate solution given by HAM for theExample 2: left: The exact solution and right: uapprox[10](t,r; −0.61).

Fig. 7. Comparison between the exact solution and the approximate solution given by HAM uapprox[5](t,r; −0.61)forExample 2at t=1. 7. Conclusion

In this paper, the homotopy analysis method (HAM) was made applicable to linear fuzzy Fredholm integral equations of the second kind. For the kernels with difficulties to determine the sign, we introduced a new parametric form of FFIE. Also, in Section4, the HAM employed for linear system of Fredholm integral equations independently which can be extended for the other types of system of integral equations such as nonlinear system of Fredholm integral equations and linear/nonlinear system of Volterra-type integral equations. The results have shown the validity and the great potential of HAM to solve linear fuzzy Fredholm integral equations.

Acknowledgements

The authors would like to thank the anonymous referees, especially the third referee, for his/her constructive comments and suggestions. Many thanks are due to the Iran University of Science and Technology and Ilam University of Iran for financial support.

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