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Research Article | Open Access

Volume 2013 |Article ID 584240 | https://doi.org/10.1155/2013/584240

H. Saberi Najafi, S. A. Edalatpanah, A. H. Refahi Sheikhani, "Application of Homotopy Perturbation Method for Fuzzy Linear Systems and Comparison with Adomian’s Decomposition Method", Chinese Journal of Mathematics, vol. 2013, Article ID 584240, 7 pages, 2013. https://doi.org/10.1155/2013/584240

# Application of Homotopy Perturbation Method for Fuzzy Linear Systems and Comparison with Adomian’s Decomposition Method

Accepted12 Sep 2013
Published28 Oct 2013

#### Abstract

We present an efficient numerical algorithm for solution of the fuzzy linear systems (FLS) based on He’s homotopy perturbation method (HPM). Moreover, the convergence properties of the proposed method have been analyzed and also comparisons are made between Adomian’s decomposition method (ADM) and the proposed method. The results reveal that our method is effective and simple.

#### 1. Introduction

Consider the following linear system: where denotes a vector in a finite-dimensional space and . Linear systems are of fundamental importance in various fields of science and engineering and there are numerous methods to find a solution for them; see  and the references therein.

When coefficients of the system are imprecise, accessing the solution with no ambiguity will be difficult. Therefore, fuzzy logic was proposed by Zadeh (1965) and encountered seriously over Aristotle classic logic. Professor Zadeh discussed fuzzy set theory, fuzzy number concept, and arithmetical operation with these numbers in  and following that many articles and books were published in fuzzy systems.

We refer the reader to  for more information on fuzzy numbers and fuzzy arithmetic. Fuzzy systems are used to study a variety of problems including fuzzy metric spaces , fuzzy differential equations , particle physics [16, 17], game theory , optimization , and especially fuzzy linear systems . Fuzzy number arithmetic is widely applied and useful in computation of linear system whose parameters are all or partially represented by fuzzy numbers. Friedman et al.  introduced a general model for solving a fuzzy linear system whose coefficient matrix is crisp and the right-hand side column is an arbitrary fuzzy number vector. They used the parametric form of fuzzy numbers and replaced the original fuzzy linear system by a crisp linear system. Furthermore, some iterative methods have been presented for solving such fuzzy linear systems .

In this paper, we solve fuzzy linear systems (FLS) via an analytical method called homotopy perturbation method (HPM). This method was first proposed by the Chinese mathematician He in 1999  and was further developed and improved by him . This author presented a homotopy perturbation technique based on the introduction of homotopy in topology coupled with the traditional perturbation method for the solution of algebraic equations. This technique provides a summation of an infinite series with easily computable terms, which converges rapidly to the solution of the problem. In the literature, various authors have successfully applied this method for any kinds of different problems . The HPM has some advantages over routine numerical methods. This method does not involve discretization of the variables and hence is free from rounding off errors and does not require large computer memory or time. In this paper we focus on the use of the homotopy perturbation method for solving the FLS.

#### 2. Fuzzy Linear Systems (FLS)

In this section we provide some basic notations and definitions of fuzzy number and fuzzy linear system.

Definition 1. An arbitrary fuzzy number is represented, in parametric form, by an ordered pair of functions , , which satisfy the following requirements (see ):(i) is a bounded monotonic increasing left continuous function over ;(ii) is a bounded monotonic decreasing left continuous function over ;(iii), . A crisp number can be simply expressed as .
The addition and scalar multiplication of fuzzy numbers and can be described as follows:(i) if and only if and ,(ii), (iii).

Definition 2. Consider the following linear system of equations: where the coefficient matrix , , , is a crisp matrix and ; is called a fuzzy linear system (FLS).

Definition 3. A fuzzy number vector , given by parametric form , , , is called a solution of the fuzzy linear system (2) if Friedman et al.  in order to solve the system given by (3) have solved a crisp linear system as where are determined as follows: And the others are zero. Then refering to  we have Or where , and .

#### 3. Homotopy Perturbation Method for Linear Systems

Consider (1), where Let also and , where is a known vector. Then we define homotopy as follows: where is an embedding parameter and is nonzero auxiliary parameter.

Obviously, we will have According to the HPM, we can first use the embedding parameter as a small parameter and assume that the solution of (1) can be written as a power series in : and the exact solution is obtained as follows: Putting (11) into (9) and comparing the coefficients of identical degrees of on both sides, we find And in general, Taking yields. Therefore the solution can be in the following form: The convergence of the series equation (16) when is proved for diagonally dominant matrix in .

#### 4. HPM for the Fuzzy Linear Systems

Asady and Mansouri in  proposed a method for finding a fuzzy solution of FLS without increasing the order. In this method the original system with a matrix is replaced by two crisp linear systems. Furthermore, improvement of computing time compared with other methods by using this method is shown. This method is given by the following.

By noting the structure of we have the following linear system: Therefore, by adding and then subtracting the two parts of (17) we obtain the following: where Thus, by adding and subtracting the two solutions of systems equation (18) we have the following: Now, we solve the FLS using HPM.

By using the above model, HPM method for FLS is as follows: where is the diagonal part of and , are, respectively, strictly lower and upper triangular matrices of .

So as iterative method we have Algorithm 1.

 Given a stopping criterion with tolerance , for , until do; , , Obtain End for.

Next, we will present the convergence analysis of this method.

Definition 4 (see [1, 4, 68]). (a) A matrix is called a -matrix if for any , .
(b) A -matrix is a nonsingular -matrix, if is nonsingular and if .
(c) For any matrix the comparison matrix is defined by
(d) Matrix is an -matrix if and only if is a nonsingular -matrix.

Definition 5 (see [1, 4, 68]). Let be a real matrix. The splitting is called;(a)convergent if ;(b)-splitting if is a nonsingular -matrix and .

Lemma 6 (see ). Let be an -splitting of . Then if and only if is a nonsingular -matrix.

Lemma 7 (see ). If is an -matrix, then .

Theorem 8. The series in HPM method for solving linear system convergence if   and is an -matrix.

Proof. It is well known that the series convergence if and only if .
Furthermore, we have Since and is an -matrix, we see that is also an -matrix, is an -matrix, and is -splitting; by Definition 5 and Lemma 6 we have Furthermore, from Lemma 7, Therefore, And the proof is completed.

Theorem 9. Algorithm 1 for solving the fuzzy linear system converges if   and is an -matrix.

Proof. We need to show that Also it is easy to see that if is an -matrix, then   is and -matrix too.
Therefore by Theorem 8 the proof is completed.

#### 5. Comparison Results

Adomian’s decomposition method (ADM) was first introduced by G. Adomian in the beginning of 1980s [44, 45] and has been rapidly growing in recent years. In this method the solution of a functional equation is considered as the sum of an infinite series usually converging to an accurate solution. Allahviranloo in  applied the Adomian decomposition method to solve the fuzzy linear systems by model of (6) and has shown that this method is equivalent to the Jacobi iterative method. But since Flop count and the computing time in model (18) are shorter than model (6) we present ADM for FLS based on model (18). This algorithm is as Algorithm 2.

 Given and the stopping criterion with tolerance , for , until  do , , Obtain End for.

Now, to show the ability of these algorithms we test the following FLS, using Matlab software.

Example 10. Consider the following fuzzy system The tables show the numerical results of the above example. In Table 1, we reported the number of iterations (ITER) for the HPM and ADM methods with different . Also .

 ITER (HAM) ITER (ADM) 10 23 32 20 41 62 30 55 91 40 66 119 50 80 147 60 93 177 70 106 204 80 119 233 90 132 267 100 147 290

From Table 1, we can see that the number of iterations in HPM algorithm is superior to the number of iterations in ADM algorithm.

Tables 2 and 3 also show comparison of accumulated errors for system (28) obtained using HPM and ADM.

 Error (HAM) Error (ADM) 0.0 0.006238873 0.1 0.006840745 0.2 0.007442617 0.3 0.008044489 0.4 0.008646361 0.5 0.009248233 0.6 0.009850104 0.7 0.010451976 0.8 0.011053848 0.9 0.001023488 0.011655720 1.0 0.001131620 0.012257592
 Error (HAM) Error (ADM) 0.0 0.006207893 0.1 0.006833297 0.2 0.007458701 0.3 0.008084104 0.4 0.008709507 0.5 0.009334911 0.6 0.009960314 0.7 0.010585718 0.8 0.001014137 0.011211121 0.9 0.001106668 0.011836525 1.0 0.001199200 0.012461928

From the above tables, we can see that the results of the HPM are close to the exact solution in comparison with ADM which confirm the validity of our method.

#### 6. Conclusions

The homotopy perturbation method (HPM) is a powerful tool which is capable of handling linear/nonlinear equations. In this paper, we proposed homotopy perturbation method and compared it with Adomian’s decomposition method (ADM) for solving fuzzy linear systems (FLS). This method does not require parameter in any equation, the same as the perturbation approach, and also is very simple and straightforward. The numerical results show that the results of the present method are in excellent agreement with those of exact solution and reductions in the size of calculations compared with the ADM.

#### Acknowledgments

The authors would like to thank all the three reviewers for their valuable suggestions which have led to an improvement in both the quality and clarity of this paper.

1. R. S. Varga, Matrix Iterative Analysis, Springer, Berlin, Germany, 2nd edition, 2000.
2. D. M. Young, Iterative Solution of Large Linear Systems, Academic Press, New York, NY, USA, 1971.
3. Y. Saad, Iterative Methods for Sparse Linear Systems, SIAM, Philadelphia, Pa, USA, 2003.
4. H. S. Najafi and S. A. Edalatpanah, “On the convergence regions of generalized AOR methods for linear complementarity problems,” Journal of Optimization Theory and Applications, vol. 156, pp. 859–866, 2013. View at: Google Scholar
5. H. S. Najafi and S. A. Edalatpanah, “A new modified SSOR ieration method for solving augmented linear systems,” International Journal of Computer Mathematics, 2013. View at: Publisher Site | Google Scholar
6. H. S. Najafi and S. A. Edalatpanah, “Comparison analysis for improving preconditioned SOR-type iterative method,” Numerical Analysis and Applications, vol. 6, pp. 62–70, 2013. View at: Google Scholar
7. H. S. Najafi and S. A. Edalatpanah, “Iterative methods with analytical preconditioning technique to linear complementarity problems: application to obstacle problems,” RAIRO, vol. 47, pp. 59–71, 2013. View at: Google Scholar
8. H. S. Najafi and S. A. Edalatpanad, “On application of Liao’s method for system of linear equations,” Ain Shams Engineering Journal, vol. 4, pp. 501–505, 2013. View at: Google Scholar
9. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. View at: Google Scholar
10. L. A. Zadeh, “A fuzzy-set-theoretic interpretation of linguistic hedges,” Journal of Cybernetics, vol. 2, no. 3, pp. 4–34, 1972. View at: Google Scholar
11. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning-I,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975. View at: Google Scholar
12. L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems, vol. 1, no. 1, pp. 3–28, 1978. View at: Google Scholar
13. A. Kaufmann and M. M. Gupta, Introduction Fuzzy Arithmetic, Van Nostrand Reinhold, New York, NY, USA, 1985.
14. J. H. Park, “Intuitionistic fuzzy metric spaces,” Chaos, Solitons and Fractals, vol. 22, no. 5, pp. 1039–1046, 2004. View at: Publisher Site | Google Scholar
15. M. Friedman, M. Ming, and A. Kandel, “Numerical solutions of fuzzy differential and integral equations,” Fuzzy Sets and Systems, vol. 106, no. 1, pp. 35–48, 1999. View at: Google Scholar
16. M. S. Elnaschie, “A review of E infinity theory and the mass spectrum of high energy particle physics,” Chaos, Solitons and Fractals, vol. 19, no. 1, pp. 209–236, 2004. View at: Publisher Site | Google Scholar
17. M. S. Elnaschie, “The concepts of E infinity: an elementary introduction to the Cantorian-fractal theory of quantum physics,” Chaos, Solitons and Fractals, vol. 22, no. 2, pp. 495–511, 2004. View at: Publisher Site | Google Scholar
18. H. S. Najafi and S. A. Edalatpanad, “On the Nash equilibrium solution of fuzzy bimatrix games,” International Journal of Fuzzy Systems and Rough Systems, vol. 5, no. 2, pp. 93–97, 2012. View at: Google Scholar
19. H. S. Najafi and S. A. Edalatpanad, “A note on ‘a new method for solving fully fuzzy linear programming problems’,” Applied Mathematical Modelling, vol. 37, pp. 7865–7867, 2013. View at: Google Scholar
20. M. Friedman, M. Ming, and A. Kandel, “Fuzzy linear systems,” Fuzzy Sets and Systems, vol. 96, no. 2, pp. 201–209, 1998. View at: Google Scholar
21. D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, NewYork, NY, USA, 1980.
22. D. Dubois and H. Prade, “Systems of linear fuzzy constraints,” Fuzzy Sets and Systems, vol. 3, no. 1, pp. 37–48, 1980. View at: Google Scholar
23. J. J. Buckley and Y. Qu, “Solving systems of linear fuzzy equations,” Fuzzy Sets and Systems, vol. 43, no. 1, pp. 33–43, 1991. View at: Google Scholar
24. M. Friedman, M. Ming, and A. Kandel, “Duality in fuzzy linear systems,” Fuzzy Sets and Systems, vol. 109, pp. 55–58, 2000. View at: Google Scholar
25. T. Allahviranloo, “Numerical methods for fuzzy system of linear equations,” Applied Mathematics and Computation, vol. 155, no. 2, pp. 493–502, 2004. View at: Publisher Site | Google Scholar
26. T. Allahviranloo, “The Adomian decomposition method for fuzzy system of linear equations,” Applied Mathematics and Computation, vol. 163, no. 2, pp. 553–563, 2005. View at: Publisher Site | Google Scholar
27. M. Dehghan and B. Hashemi, “Iterative solution of fuzzy linear systems,” Applied Mathematics and Computation, vol. 175, no. 1, pp. 645–674, 2006. View at: Publisher Site | Google Scholar
28. K. Wang and B. Zheng, “Symmetric successive overrelaxation methods for fuzzy linear systems,” Applied Mathematics and Computation, vol. 175, no. 2, pp. 891–901, 2006. View at: Publisher Site | Google Scholar
29. B. Asady and P. Mansouri, “Numerical solution of fuzzy linear system,” International Journal of Computer Mathematics, vol. 86, no. 1, pp. 151–162, 2009. View at: Publisher Site | Google Scholar
30. H. S. Najafi and S. A. Edalatpanah, “The block AOR iterative methods for solving fuzzy linear systems,” The Journal of Mathematics and Computer Science, vol. 4, pp. 527–535, 2012. View at: Google Scholar
31. H. S. Najafi and S. A. Edalatpanah, “Preconditioning strategy to solve fuzzy linear systems (FLS),” International Review of Fuzzy Mathematics, vol. 7, no. 2, pp. 65–80, 2012. View at: Google Scholar
32. H. S. Najafi and S. A. Edalatpanad, “An improved model for iterative algorithms in fuzzy linear systems,” Computational Mathematics and Modeling, vol. 24, pp. 443–451, 2013. View at: Google Scholar
33. J. He, “Homotopy perturbation technique,” Computer Methods in Applied Mechanics and Engineering, vol. 178, no. 3-4, pp. 257–262, 1999. View at: Google Scholar
34. J. He, “Homotopy perturbation method: a new nonlinear analytical technique,” Applied Mathematics and Computation, vol. 135, no. 1, pp. 73–79, 2003. View at: Publisher Site | Google Scholar
35. J. He, “Application of homotopy perturbation method to nonlinear wave equations,” Chaos, Solitons and Fractals, vol. 26, no. 3, pp. 695–700, 2005. View at: Publisher Site | Google Scholar
36. J. He, “Homotopy perturbation method for solving boundary value problems,” Physics Letters A, vol. 350, no. 1-2, pp. 87–88, 2006. View at: Publisher Site | Google Scholar
37. A. Beléndez, A. Hernández, T. Beléndez, E. Fernández, M. L. Alvarez, and C. Neipp, “Application of He’s homotopy perturbation method to the Duffing-harmonic oscillator,” The International Journal of Nonlinear Sciences and Numerical Simulation, vol. 8, pp. 79–88, 2007. View at: Google Scholar
38. T. Öziş and A. Yildirim, “A note on He's homotopy perturbation method for van der Pol oscillator with very strong nonlinearity,” Chaos, Solitons and Fractals, vol. 37, no. 3, pp. 989–991, 2007. View at: Publisher Site | Google Scholar
39. J. I. Ramos, “Series approach to the Lane-Emden equation and comparison with the homotopy perturbation method,” Chaos, Solitons and Fractals, vol. 38, no. 2, pp. 400–408, 2008. View at: Publisher Site | Google Scholar
40. L. Cveticanin, “Application of homotopy-perturbation to non-linear partial differential equations,” Chaos, Solitons and Fractals, vol. 40, no. 1, pp. 221–228, 2009. View at: Publisher Site | Google Scholar
41. B. Keramati, “An approach to the solution of linear system of equations by He's homotopy perturbation method,” Chaos, Solitons and Fractals, vol. 41, no. 1, pp. 152–156, 2009. View at: Publisher Site | Google Scholar
42. M. Mehrabinezhad and J. Saberi-Nadjafi, “Application of He's homotopy perturbation method to linear programming problems,” International Journal of Computer Mathematics, vol. 88, no. 2, pp. 341–347, 2011. View at: Publisher Site | Google Scholar
43. A. Frommer and D. B. Szyld, “H-splittings and two-stage iterative methods,” Numerische Mathematik, vol. 63, no. 1, pp. 345–356, 1992. View at: Publisher Site | Google Scholar
44. G. Adomian, Solving Frontier Problems of Physics: The Decomposition Method, Kluwer Academic, Dordrecht, The Netherlands, 1994.
45. G. Adomian and R. Rach, “On the solution of algebraic equations by the decomposition method,” Journal of Mathematical Analysis and Applications, vol. 105, no. 1, pp. 141–166, 1985. View at: Google Scholar

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