Abstract and Applied Analysis
Volume 2014 (2014), Article ID 957590, 4 pages
http://dx.doi.org/10.1155/2014/957590

Research Article

Approximate Solutions of Fractional Riccati Equations Using the Adomian Decomposition Method

Fei Wu¹ and Lan-Lan Huang²

¹Key Laboratory of Energy Engineering Safety and Disaster Mechanics, Ministry of Education, School of Architecture and Environment, Sichuan University, Chengdu 610065, China
²Institute of Applied Nonlinear Science, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641112, China

Received 8 June 2014; Accepted 29 August 2014; Published 14 December 2014

Academic Editor: Guotao Wang

Copyright © 2014 Fei Wu and Lan-Lan Huang. 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.

The fractional derivative equation has extensively appeared in various applied nonlinear problems and methods for finding the model become a popular topic. Very recently, a novel way was proposed by Duan (2010) to calculate the Adomian series which is a crucial step of the Adomian decomposition method. In this paper, it was used to solve some fractional nonlinear differential equations.

1. Introduction

The fractional derivative has good memory effects compared with the ordinary calculus. In view of this point, it has been proven as a good tool in nonlinear science, for example, the anomalous diffusion [1, 2], the material’s viscoelasticity [3, 4], the chaotic behaviors of biology population [5, 6], and so forth.

However, everything has two sides. The fractional derivative’s memory effects also lead to the numerical solutions’ accumulative errors. Many nonlinear techniques cannot perform the same role as those in ordinary differential equation. For example, the variational iteration method cannot be applied due to the fact that the integral by parts cannot hold and the Lagrange multipliers there are not easily identified; in the Adomian decomposition method, the Adomian series cannot be expanded large enough which greatly affects the solutions’ accuracies and even five- or six-order approximation becomes impossible (see the analysis in [7]).

Very recently and fortunately, for the ADM, Duan [8, 9] proposed a convenient way to calculate Adomian series which is the main and crucial step of the classical ADM developed by Adomian. This way can rapidly decompose the nonlinear terms and some new high order approximation schemes for nonlinear differential equations are proposed [10]. The technique has been successfully extended to fractional differential equations and boundary value problems [11, 12].

In this paper, we investigate the following fractional nonlinear differential equation:

where is the Caputo derivative with respect to .

The paper is organized as follows: Section 2 introduces some basics of the ADM and the fractional calculus; Section 3 considers the differential equation from the case in (1)

and gives the analytical formula.

2. Preliminaries

Definition 1 (see [13]). The Caputo derivative is defined as

where is the Gamma function.

Definition 2 (see [14]). The R-L integration of order is defined by

Generally, consider the following nonlinear equation:

where is the highest derivative, is the remaining linear part containing the lower order derivatives, and is the nonlinear operator.

Apply the inverse of the linear operator in (5) and we can obtain

Assume that

and expand the term approximately as

where the is calculated by

As a result, one can obtain the analytical iteration scheme as

Here we conclude the application of the ADM: firstly, one needs to have equivalent integral form of the original governing equations; second, decompose the nonlinear terms into linear ones and determine the iteration schemes; third, obtain the solutions successively. For the other applications and modified versions, we do not introduce any more here. The readers who feel interested in the development of the method are referred to [7, 12, 15–19].

3. Numerical Schemes of Integer Equations

According to the ADM, we first establish the integral equation as the one

If we directly use the classical ADM’s idea we can obtain the formula

The explicit solution of (2) was found to be [20]

We can assume that

As a result, from the iteration equations (10) and (12), we can obtain the approximate solutions in comparison with (13).

We find that the approximate solution has a good agreement with the explicit solution in Figure 1. So the scheme is efficient and useful.

Figure 1: “*” the 20th order approximate solution; “–” the explicit solution.

4. Numerical Schemes of Fractional Equations

Case 1 (). Similarly, we can have the integral equation for (1):

If we directly use the classical ADM’s idea we can obtain the formula

Here , we give the first solutions as

For simplicity, we define the residual function as

and illustrate the errors for and in Figure 2.

Figure 2: “–” the residual function.

Case 2 (). Consider the following

Here we need two initial conditions and . We set

Similarly, we can have the iteration formula as

whose error analysis is given in Figure 3. We can see that the approximate solution is reliable.

Figure 3: “–” the residual function.

We plot the approximate solution in Figure 4 in the interval .

Figure 4: “*” the 30th order approximate solution of (19).

5. Conclusions

This paper applied the famous Adomian decomposition method to FDEs whose fractional order varied from 0 to 2. First, we revisit the method for integer equation. We conclude the general steps and use the method to solve FDEs analytically. Through the analysis of the residual function, it can be concluded that the ADM using Duan’s way to calculate the Adomian series is very suitable and efficient for obtaining analytical solutions of fractional nonlinear differential equations.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This work is financially supported by the National Natural Science Foundation (Grant nos. 51120145001, 51374148, and 51104101).

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