# Numerical Solution of Nonlinear Fredholm Integrodifferential Equations by Hybrid of Block-Pulse Functions and Normalized Bernstein Polynomials

Authors: S. H. Behiry
Journal: Abstract and Applied Analysis
 (2013)

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## Abstract

A numerical method for solving nonlinear Fredholm integrodifferential equations is proposed. The method is based on hybrid functions approximate. The properties of hybrid of block pulse functions and orthonormal Bernstein polynomials are presented and utilized to reduce the problem to the solution of nonlinear algebraic equations. Numerical examples are introduced to illustrate the effectiveness and simplicity of the present method.


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## Body

## 1. Introduction

Integrodifferential equations are often involved in mathematical formulation of physical phenomena. Fredholm integrodifferential equations play an important role in many fields such as economics, biomechanics, control, elasticity, fluid dynamics, heat and mass transfer, oscillation theory, and airfoil theory; for examples see [1–3] and references cited therein. Finding numerical solutions for Fredholm integrodifferential equations is one of the oldest problems in applied mathematics. Numerous works have been focusing on the development of more advanced and efficient methods for solving integrodifferential equations such as wavelets method [4, 5], Walsh functions method [6], sinc-collocation method [7], homotopy analysis method [8], differential transform method [9], the hybrid Legendre polynomials and block-pulse functions [10], Chebyshev polynomials method [11], and Bernoulli matrix method [12].Block-pulse functions have been studied and applied extensively as a basic set of functions for signals and functions approximations. All these studies and applications show that block-pulse functions have definite advantages for solving problems involving integrals and derivatives due to their clearness in expressions and their simplicity in formulations; see [13]. Also, Bernstein polynomials play a prominent role in various areas of mathematics. Many authors have used these polynomials in the solution of integral equations, differential equations, and approximation theory; see for instance [14–17].The purpose of this work is to utilize the hybrid functions consisting of combination of block-pulse functions with normalized Bernstein polynomials for obtaining numerical solution of nonlinear Fredholm integrodifferential equation:(1)∑i=0spi(x)y(i)(x)=g(x)+λ∫01k(x,t)[y(t)]qdt,hhhhhhhhhhhhh0≤x,t<1,
with the conditions
(2)y(i)(0)=αi,0≤i≤s-1,
where y(i)(x) is the ith derivative of the unknown function that will be determined, k(x,t) is the kernel of the integral, g(x) and pi(x) are known analytic functions, q is a positive integer,  and λ and αi are suitable constants. The proposed approach for solving this problem uses few numbers of basis and benefits of the orthogonality of block-pulse functions and the advantages of orthonormal Bernstein polynomials properties to reduce the nonlinear integrodifferential equation to easily solvable nonlinear algebraic equations.This paper is organized as follows. In the next section, we present Bernstein polynomials and hybrid of block-pulse functions. Also, their useful properties such as functions approximation, convergence analysis, operational matrix of product, and operational matrix of differentiation are given. In Section3, the numerical scheme for the solution of (1) and (2) is described. In Section 4, the proposed method is applied to some nonlinear Fredholm integrodifferential equations, and comparisons are mad with the existing analytic or numerical solutions that were reported in other published works in the literature. Finally conclusions are given in Section 5.


## 2. Properties of Hybrid Functions

### 2.1. Hybrid of Block-Pulse Functions and Orthonormal Bernstein Polynomials

The Bernstein polynomials ofnth degree are defined on the interval [0,1] as [16]
(3)Bi,n(x)=(ni)xi(1-x)n-i,fori=0,1,2,…,n,
where
(4)(ni)=n!i!(n-i)!.
There are (n+1)  nth degree Bernstein polynomials. Using Gram-Schmidt orthonormalization process on Bi,n(x), we obtain a class of orthonormal polynomials from the Bernstein polynomials. We call them orthonormal Bernstein polynomials of degree n and denote them by bi,n(x), 0≤i≤n. For n=3, the four orthonormal Bernstein polynomials are given by
(5)b0,3(x)=-7[x3-3x2+3x-1],b1,3(x)=5[7x3-15x2+9x-1],b2,3(x)=-3[21x3-33x2+13x-1],b3,3(x)=35x3-45x2+15x-1.Hybrid functionshji(x),j=1,2,…,m and i=0,1,…,n are defined on the interval [0,1) as
(6)hji(x)={mbi,n(mx-j+1),j-1m≤x<jm,0,otherwise,
where j and n are the order of block-pulse functions and degree of orthonormal Bernstein polynomials, respectively.It is clear that these sets of hybrid functions in (6) are orthonormal and disjoint.


### 2.2. Functions Approximation

A functiony(x)∈L2[0,1) may be approximated as
(7)y(x)≈∑j=1m∑i=0ncjihji(x)=CTH(x),
where
(8)C=[C1T,C2T,…,CjT,…,CmT]T,Cj=[cj0,cj1,cj2,…,cjn]T,j=1,2,…,m,(9)H(x)=[H1T(x),H2T(x),…,HjT(x),…,HmT(x)]T,
and Hj(x)=[hj0(x),hj1(x),…,hjn(x)]T, j=1,2,…,m. The constant coefficients cji are (y(x),hji(x)),  i=0,1,2,…,n, j=1,2,…,m, and (·,·) is the standard inner product on L2[0,1).We can also approximate the functionk(x,t)∈L2([0,1)×[0,1)) by
(10)k(x,t)≈∑i=1m∑j=1m∑l=0n∑r=0n‍klrijhil(x)hjr(t)=HT(x)KH(t),
where K=[Kij] is an m(n+1)×m(n+1) matrix, such that the elements of the sub matrix kij are
(11)klrij=∫i-1/mi/m∫j-1/mj/mk(x,t)hi(l-1)(x)hj(r-1)(t)dxdt,ffffhffl,r=1,2,…,n+1,i,j=1,2,…,m,
utilizing properties of block-pulse function and orthonormal Bernstein polynomials.


### 2.3. Convergence Analysis

In this section, the error bound and convergence is established by the following lemma.Lemma 1.
Suppose thatf∈C(n+1)[0,1) is n+1 times continuously differentiable function such that f=∑j=1mfj, and let Yj=Span{hj0(x),hj1(x),…,hjn(x)}, j=1,2,…,m. If CjTHj(x) is the best approximation to fj from Yj, then CTH(x) approximates f with the following error bound:
(12)∥f-CTH(x)∥2≤γmn+1(n+1)!2n+3,γ=maxx∈[0.1)|f(n+1)(x)|.Proof.
The Taylor expansion for the functionfj(x) is
(13)f~j(x)=fj(j-1m)+fj′(j-1m)(x-j-1m)+⋯+fj(n)(j-1m)(x-(j-1/m))nn!,nnnnnnnnnhhhhhnnnnnnj-1m≤x<jm,
for which it is known that
(14)|fj(x)-f~j(x)|≤|f(n+1)(η)|(x-(j-1/m))n+1(n+1)!,mmmvvvhhhmmη∈[j-1m,jm),j=1,2,…,m.
Since CjTHj(x) is the best approximation to fj form Yj and f~j∈Yj, using (14) we have
(15)∥fj-CjTHj(x)∥22≤∥fj-f~j∥2=∫j-1/mj/m|fj(x)-f~j(x)|2dx≤∫j-1/mj/m[f(n+1)(η)(x-(j-1/m))n+1(n+1)!]2dx≤[γ(n+1)!]2∫j-1/mj/m(x-j-1m)2n+2dx=[γ(n+1)!]21m2n+3(2n+3).
Now,
(16)∥f-CTH(x)∥22≤∑j=1m∥fj-CjTHj(x)∥22≤γ2m2n+2[(n+1)!]2(2n+3).
By taking the square roots we have the above bound.


### 2.4. The Operational Matrix of Product

In this section, we present a general formula for finding them(n+1)×m(n+1) operational matrix of product C~ whenever
(17)CTH(x)HT(x)≈HT(x)C~,
where
(18)C~=diag[C~1,C~2,…,C~j,…,C~m].
In (18), C~j=[clrj] are (n+1)×(n+1) symmetric matrices depending on n, where
(19)clrj=∫j-1/mj/m(hj(l-1)(x)hj(r-1)(x)∑i=0ncjihji(x))dx,hhhhhhhhhhhhhhhhhhhhl,r=1,2,…,n+1.Furthermore, the integration of cross-product of two hybrid functions vectors is(20)∫01H(x)HT(x)dx=I,
where I is the m(n+1) identity matrix.


### 2.5. The Operational Matrix of Differentiation

The operational matrix of derivative of the hybrid functions vectorH(x) is defined by
(21)ddxH(x)=DH(x),
where D is the m(n+1)×m(n+1) operational matrix of derivative given as
(22)H(x)=[H1T(x),H2T(x),…,HjT(x),…,HmT(x)]T=A~T~(x),
where A~=diag[A1,A2,…,Aj,…,Am] is the m(n+1)×m(n+1) coefficient matrix of the (n+1)×(n+1) coefficient submatrix Aj, and T~(x)=[t1(x),t2(x),…,tj(x),…,tm(x)]T is the m(n+1) vector with tj(x)=[1,x,x2,…,xn]T, such that Hj(x)=Ajtj(x). Now
(23)ddxH(x)=A~Q~T~(x)=A~Q~A~-1H(x),
where Q~=diag[Q,…,Q] is the m(n+1)×m(n+1) matrix of the (n+1)×(n+1) sub-matrix Q, such that
(24)Q=[000⋯00100⋯00020⋯00⋮⋮⋮⋯⋮⋮000⋯n0].
Hence,
(25)D=A~Q~A~-1.
In general, we can have
(26)dkdxkH(x)=DkH(x),k=1,2,3,….


## 2.1. Hybrid of Block-Pulse Functions and Orthonormal Bernstein Polynomials

The Bernstein polynomials ofnth degree are defined on the interval [0,1] as [16]
(3)Bi,n(x)=(ni)xi(1-x)n-i,fori=0,1,2,…,n,
where
(4)(ni)=n!i!(n-i)!.
There are (n+1)  nth degree Bernstein polynomials. Using Gram-Schmidt orthonormalization process on Bi,n(x), we obtain a class of orthonormal polynomials from the Bernstein polynomials. We call them orthonormal Bernstein polynomials of degree n and denote them by bi,n(x), 0≤i≤n. For n=3, the four orthonormal Bernstein polynomials are given by
(5)b0,3(x)=-7[x3-3x2+3x-1],b1,3(x)=5[7x3-15x2+9x-1],b2,3(x)=-3[21x3-33x2+13x-1],b3,3(x)=35x3-45x2+15x-1.Hybrid functionshji(x),j=1,2,…,m and i=0,1,…,n are defined on the interval [0,1) as
(6)hji(x)={mbi,n(mx-j+1),j-1m≤x<jm,0,otherwise,
where j and n are the order of block-pulse functions and degree of orthonormal Bernstein polynomials, respectively.It is clear that these sets of hybrid functions in (6) are orthonormal and disjoint.


## 2.2. Functions Approximation

A functiony(x)∈L2[0,1) may be approximated as
(7)y(x)≈∑j=1m∑i=0ncjihji(x)=CTH(x),
where
(8)C=[C1T,C2T,…,CjT,…,CmT]T,Cj=[cj0,cj1,cj2,…,cjn]T,j=1,2,…,m,(9)H(x)=[H1T(x),H2T(x),…,HjT(x),…,HmT(x)]T,
and Hj(x)=[hj0(x),hj1(x),…,hjn(x)]T, j=1,2,…,m. The constant coefficients cji are (y(x),hji(x)),  i=0,1,2,…,n, j=1,2,…,m, and (·,·) is the standard inner product on L2[0,1).We can also approximate the functionk(x,t)∈L2([0,1)×[0,1)) by
(10)k(x,t)≈∑i=1m∑j=1m∑l=0n∑r=0n‍klrijhil(x)hjr(t)=HT(x)KH(t),
where K=[Kij] is an m(n+1)×m(n+1) matrix, such that the elements of the sub matrix kij are
(11)klrij=∫i-1/mi/m∫j-1/mj/mk(x,t)hi(l-1)(x)hj(r-1)(t)dxdt,ffffhffl,r=1,2,…,n+1,i,j=1,2,…,m,
utilizing properties of block-pulse function and orthonormal Bernstein polynomials.


## 2.3. Convergence Analysis

In this section, the error bound and convergence is established by the following lemma.Lemma 1.
Suppose thatf∈C(n+1)[0,1) is n+1 times continuously differentiable function such that f=∑j=1mfj, and let Yj=Span{hj0(x),hj1(x),…,hjn(x)}, j=1,2,…,m. If CjTHj(x) is the best approximation to fj from Yj, then CTH(x) approximates f with the following error bound:
(12)∥f-CTH(x)∥2≤γmn+1(n+1)!2n+3,γ=maxx∈[0.1)|f(n+1)(x)|.Proof.
The Taylor expansion for the functionfj(x) is
(13)f~j(x)=fj(j-1m)+fj′(j-1m)(x-j-1m)+⋯+fj(n)(j-1m)(x-(j-1/m))nn!,nnnnnnnnnhhhhhnnnnnnj-1m≤x<jm,
for which it is known that
(14)|fj(x)-f~j(x)|≤|f(n+1)(η)|(x-(j-1/m))n+1(n+1)!,mmmvvvhhhmmη∈[j-1m,jm),j=1,2,…,m.
Since CjTHj(x) is the best approximation to fj form Yj and f~j∈Yj, using (14) we have
(15)∥fj-CjTHj(x)∥22≤∥fj-f~j∥2=∫j-1/mj/m|fj(x)-f~j(x)|2dx≤∫j-1/mj/m[f(n+1)(η)(x-(j-1/m))n+1(n+1)!]2dx≤[γ(n+1)!]2∫j-1/mj/m(x-j-1m)2n+2dx=[γ(n+1)!]21m2n+3(2n+3).
Now,
(16)∥f-CTH(x)∥22≤∑j=1m∥fj-CjTHj(x)∥22≤γ2m2n+2[(n+1)!]2(2n+3).
By taking the square roots we have the above bound.


## 2.4. The Operational Matrix of Product

In this section, we present a general formula for finding them(n+1)×m(n+1) operational matrix of product C~ whenever
(17)CTH(x)HT(x)≈HT(x)C~,
where
(18)C~=diag[C~1,C~2,…,C~j,…,C~m].
In (18), C~j=[clrj] are (n+1)×(n+1) symmetric matrices depending on n, where
(19)clrj=∫j-1/mj/m(hj(l-1)(x)hj(r-1)(x)∑i=0ncjihji(x))dx,hhhhhhhhhhhhhhhhhhhhl,r=1,2,…,n+1.Furthermore, the integration of cross-product of two hybrid functions vectors is(20)∫01H(x)HT(x)dx=I,
where I is the m(n+1) identity matrix.


## 2.5. The Operational Matrix of Differentiation

The operational matrix of derivative of the hybrid functions vectorH(x) is defined by
(21)ddxH(x)=DH(x),
where D is the m(n+1)×m(n+1) operational matrix of derivative given as
(22)H(x)=[H1T(x),H2T(x),…,HjT(x),…,HmT(x)]T=A~T~(x),
where A~=diag[A1,A2,…,Aj,…,Am] is the m(n+1)×m(n+1) coefficient matrix of the (n+1)×(n+1) coefficient submatrix Aj, and T~(x)=[t1(x),t2(x),…,tj(x),…,tm(x)]T is the m(n+1) vector with tj(x)=[1,x,x2,…,xn]T, such that Hj(x)=Ajtj(x). Now
(23)ddxH(x)=A~Q~T~(x)=A~Q~A~-1H(x),
where Q~=diag[Q,…,Q] is the m(n+1)×m(n+1) matrix of the (n+1)×(n+1) sub-matrix Q, such that
(24)Q=[000⋯00100⋯00020⋯00⋮⋮⋮⋯⋮⋮000⋯n0].
Hence,
(25)D=A~Q~A~-1.
In general, we can have
(26)dkdxkH(x)=DkH(x),k=1,2,3,….


## 3. Outline of the Solution Method

This section presents the derivation of the method for solvingsth-order nonlinear Fredholm integrodifferential equation (1) with the initial conditions (2).Step  1. The functions y(i)(x), i=0,1,2,…,s are being approximated by
(27)y(i)(x)=CT(H(x))(i)=CTDiH(x),i=0,1,2,…,s,
where D is given by (25).Step  2. The function k(x,t) is being approximated by (10).Step  3. In this step, we present a general formula for approximate yq(x). By using (7) and (17), we can have
(28)y2(x)=[CTH(x)]2=CTH(x)HT(x)C=HT(x)C~C,(29)y3(x)=CTH(x)[CTH(x)]2=CTH(x)HT(x)C~C=HT(x)C~C~C=HT(x)(C~)2C,
and so by use of induction, yq(x) will be approximated as
(30)yq(x)=HT(x)(C~)q-1C.Step  4. Approximate the functions g(x) and pi(x) by
(31)g(x)≈GTH(x),(32)pi(x)≈PiTH(x),i=0,1,2,…,s,
where G and Pi are constant coefficient vectors which are defined similarly to (7).Now, using (27)–(32) and (10) to substitute into (1), we can obtain
(33)∑i=0sPiTH(x)HT(x)(Di)TC=HT(x)G+λ∫01HT(x)KH(t)HT(t)(C~)q-1Cdt.
Utilizing (17) and (20), we may have
(34)∑i=0sHT(x)P~i(Di)TC=HT(x)G+λHT(x)K(C~)q-1C,
and hence we get
(35)∑i=0sP~i(Di)TC-λK(C~)q-1C=G.
The matrix (35) gives a system of m(n+1) nonlinear algebraic equations which can be solved utilizing the initial condition for the elements of C. Once C is known, y(x) can be constructed by using (7).


## 4. Applications and Numerical Results

In this section, numerical results of some examples are presented to validate accuracy, applicability, and convergence of the proposed method. Absolute difference errors of this method is compared with the existing methods reported in the literature [5, 6, 17, 18]. The computations associated with these examples were performed using MATLAB 9.0.Example 1.
Consider the first-order nonlinear Fredholm integrodifferential equation [17, 18] as follows:
(36)y′(x)=1-13x+∫01xy2(t)dt,0≤x<1,
with the initial condition
(37)y(0)=0.
In this example, we have p0=0, p1=1, g(x)=1-(1/3)x, λ=1, k(x,t)=x, and q=2.
The matrix (35) for this example is
(38)P~1DTC-K(C~)C=G,
where for n=1 and m=2 we have(39)P~1=I,DT=[-33300-330000-33300-33],C=[c10c11c20c21],K=[116348116348316116316116143121431238183818],C~=14[36c10-2c11-2c10+6c1100-2c10+6c116c10+52c11000036c20-2c21-2c20+6c2100-2c20+6c216c20+52c21],G=[176725224763626].Equation (38) gives a system of nonlinear algebraic equations that can be solved utilizing the initial condition (37); that is, 6c10-2c11=0, we obtain
(40)c10=624,c11=28,c20=66,c21=24.
Substituting these values into (7), the result will be y(x)=x, that is, the exact solution. It is noted that the result gives the exact solution as in [17], while in [18] using the sinc method the maximum absolute error is 1.52165×10-3.Example 2.
Consider the first-order nonlinear Fredholm integrodifferential equation [6, 17] as follows:
(41)xy′(x)-y(x)=-16+45x2+∫01(x2+t)y2(t)dt,hhhhhhhhhhhhhhhhh0≤x<1.
with the initial condition
(42)y(0)=0.
In this example, we have p0=-1, p1=x, g(x)=-(1/6)+(4/5)x2, λ=1, k(x,t)=x2+t, and q=2.
The matrix (35) for this example is
(43)(P~0+P~1DT)C-K(C~)C=G,
where for n=2 and m=2 we have(44)P~0=-I,P~1=[1121560-5120000156014324000-51203245120000007121560-5120000156034324000-51203241112],DT=[-57153-25000-153-314330000-8338000000-57153-25000-153-314330000-8338],C=[c10c11c12c20c21c22],G=[-1110450-69021802310900136180192180],K=[12415457524013721520415720157211253144152411631654853144124751443165727483115720152041144171524075907151443165372111514413487372516113144112135144534819],C~=[c~100c~2],c~j=[5107cj0-5621cj1+27cj2-5621cj0+111035cj1-830105cj227cj0-830105cj1+31035cj2-5621cj0+111035cj1-830105cj2111035cj0+367cj1+27cj2-830105cj0+27cj1+5621cj227cj0-830105cj1+31035cj2-830105cj0+27cj1+5621cj231035cj0+5621cj1+1327cj2],j=1,2.Equation (43) gives a system of nonlinear algebraic equations that can be solved utilizing the initial condition (42); that is, 10c10-6c11+2c12=0, we obtain
(45)c10=10240,c11=648,c12=224,c20=1015,c21=68,c22=26.
Substituting these values into (7), the result will be y(x)=x2, that is, the exact solution. It is noted that the result gives the exact solution as in [17], while in [6] approximate solution is obtained with maximum absolute error 1.0000×10-5.Example 3.
Consider the second-order nonlinear Fredholm integrodifferential equation [17] as follows:
(46)y′′(x)+xy′(x)-xy(x)=exsinx+∫01sinx·e-2ty2(t)dt,hhhhhhhhhhhhhhhhh0≤x<1,
with the initial conditions
(47)y(0)=y′(0)=1.
The exact solution is y(x)=ex. We solve this example by using the proposed method with n=2, m=30 and  n=3, m=30. Comparison among the proposed method and methods in [17] is shown in Table 1. It is clear from this table that the results obtained by the proposed method, using few numbers of basis, are very promising and superior to that of [17].Table 1
Numerical comparison of absolute difference errors for Example3.
x
Method of [17]
The proposed method
n
=
7
n
=
2
,
m
=
30
n
=
3
,
m
=
30
0.0
3.2038
E
-
009
3.1309
E
-
007
4.0173
E
-
010
0.2
7.1841
E
-
010
3.8241
E
-
007
4.9068
E
-
010
0.4
1.4151
E
-
010
4.6707
E
-
007
5.9932
E
-
010
0.6
4.0671
E
-
011
5.7048
E
-
007
7.3201
E
-
010
0.8
9.1044
E
-
010
6.9679
E
-
007
8.9407
E
-
010
1.0
3.7002
E
-
009
8.2709
E
-
007
1.4907
E
-
010Example 4.
Consider the following nonlinear Fredholm integrodifferential equation [5, 17]:
(48)y′(x)+y(x)=12(e-2-1)+∫01y2(t)dt,0≤x<1,
with the initial conditions
(49)y(0)=1.
The exact solution of this problem is y(x)=e-x. In Table 2 we have compared the absolute difference errors of the proposed method with the collocation method based on Haar wavelets in [5] and method in [17].
Maximum absolute errors of Example4 for some different values of n and m are shown in Table 3. As it is seen from Table 3, for a certain value of n as m increases the accuracy increases, and for a certain value of m as n increases the accuracy increases as well. In case of m=1, the numerical solution obtained is based on orthonormal Bernstein polynomials only, while in case of n=0, the numerical solution obtained is based on block-pulse functions only.Table 2
Numerical comparison of absolute difference errors for Example4.
x
Method of  [5]
Method of [17]
The proposed method
Number of collocation pointsN=128
n
=
7
n
=
3
,
m
=
35
n
=
4
,
m
=
15
0.125
3.7591
E
-
007
2.4509
E
-
010
5.5200
E
-
011
1.6710
E
-
011
0.250
6.6413
E
-
007
1.0202
E
-
010
8.9982
E
-
011
3.9705
E
-
012
0.375
8.6917
E
-
007
1.6139
E
-
010
9.4606
E
-
011
1.2126
E
-
011
0.500
1.0020
E
-
006
3.2362
E
-
010
9.2457
E
-
011
1.8312
E
-
012
0.625
1.0757
E
-
006
1.9197
E
-
010
7.4991
E
-
011
8.1299
E
-
012
0.750
1.1029
E
-
006
6.6120
E
-
011
4.9442
E
-
011
7.7237
E
-
012
0.875
1.0944
E
-
006
2.2417
E
-
010
2.6083
E
-
011
2.5547
E
-
012Table 3
Maximum absolute errors for different values ofn and m for Example 4.
n
m
1
5
10
15
20
25
30
35
0
5.7735
E
-
01
1.1547
E
-
01
5.7735
E
-
02
3.8490
E
-
02
2.8868
E
-
02
2.3094
E
-
02
1.9245
E
-
02
1.6496
E
-
02
1
2.2361
E
-
01
8.9443
E
-
03
2.2361
E
-
03
9.9381
E
-
04
5.5902
E
-
04
3.5777
E
-
04
2.4845
E
-
04
1.8254
E
-
04
2
6.2994
E
-
02
5.0395
E
-
04
6.2994
E
-
05
1.8665
E
-
05
7.8743
E
-
06
4.0316
E
-
06
2.3331
E
-
06
1.4693
E
-
06
3
1.3889
E
-
02
2.2222
E
-
05
1.3889
E
-
06
2.7435
E
-
07
8.6806
E
-
08
3.5556
E
-
08
1.7147
E
-
08
9.2554
E
-
09
4
2.5126
E
-
03
8.0403
E
-
07
2.5126
E
-
08
3.3088
E
-
09
7.8519
E
-
10
2.5729
E
-
10
1.0340
E
-
10
4.7839
E
-
11
5
3.8521
E
-
04
2.4653
E
-
08
3.8521
E
-
10
3.3818
E
-
11
6.0189
E
-
12
1.5778
E
-
12
5.2841
E
-
13
2.0955
E
-
13
6
5.1230
E
-
05
6.5574
E
-
10
5.1230
E
-
12
2.9984
E
-
13
4.0023
E
-
14
8.3935
E
-
15
2.3425
E
-
15
7.9625
E
-
16


## 5. Conclusion

In this work, we present a numerical method for solving nonlinear Fredholm integrodifferential equations based on hybrid of block-pulse functions and normalized Bernstein polynomials. One of the most important properties of this method is obtaining the analytical solutions if the equation has an exact solution, that is, a polynomial function. Another considerable advantage is this method has high relative accuracy for small numbers of basisn. The matrices K, C~, and D in (10), (17), and (25), respectively, have large numbers of zero elements, and they are sparse; hence, the present method is very attractive and reduces the CPU time and computer memory. Moreover, satisfactory results of illustrative examples with respect to several other methods (e.g., Haar wavelets method, Walsh functions method, Bernstein polynomials method, and sinc collocation method) are included to demonstrate the validity and applicability of the proposed method.



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*Source: 416757-2013-10-24.xml*