Skip to main content

Dynamical Control of Computations Using the Iterative Methods to Solve Fully Fuzzy Linear Systems

  • Conference paper
  • First Online:
Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Abstract

A linear system with fuzzy coefficients matrix, unknown and right hand side fuzzy vectors is called a fully fuzzy linear system (FFLS). Solving these kinds of systems via iterative methods to find the optimal number of iterations and optimal solution is important computationally. In this study, a FFLS is solved in the stochastic arithmetic to find this optimal solution. To this end, the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method and the CADNA (Control of Accuracy and Debugging for Numerical Application) library are considered to evaluate the round-off error effect on computed results. The Gauss-Seidel, Jacobi, Richardson and SOR iterative methods are used to solve FFLS. Also, an efficient algorithm is presented based on the proposed approach to compute the optimal results. Finally, two numerical examples are solved to validate the results and show the importance of using the stochastic arithmetic in comparison with the common floating-point arithmetic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbasbandy, S., Fariborzi, M.A., Araghi, F.: A stochastic scheme for solving definite integrals. Appl. Numer. Math. 55, 125–136 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  2. Abbasbandy, S., Fariborzi, M.A., Araghi, F.: The use of the stochastic arithmetic to estimate the value of interpolation polynomial with optimal degree. Appl. Numer. Math. 50, 279–290 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. Abbasbandy, S., Fariborzi, M.A., Araghi, F.: A reliable method to determine the ill-condition functions using stochastic arithmetic. Southwest J. Pure Appl. Math. 1, 33–38 (2002)

    MathSciNet  MATH  Google Scholar 

  4. Abbasbandy, S., Fariborzi, M.A., Araghi, F.: The valid implementation of numerical integration methods. Far East J. Appl. Math. 8, 89–101 (2002)

    MathSciNet  MATH  Google Scholar 

  5. Abbsabandy, S., Jafarian, A., Ezzati, R.: Conjugate gradient method for fuzzy systematic positive definite system of linear equations. Appl. Math. Comput. 171, 1184–1191 (2005)

    MathSciNet  MATH  Google Scholar 

  6. Allahviranloo, T., Salahshour, S.: Bounded and symmetric solutions of fully fuzzy linear systems in dual form. Procedia Comput. Sci. 3, 1494–1498 (2011)

    Article  MATH  Google Scholar 

  7. Buckley, J.J., Qu, Y.: Solving systems of linear fuzzy equations. Fuzzy Sets Syst. 43, 33–43 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chesneaux, J.M.: CADNA, An ADA tool for round-off error analysis and for numerical debugging. In: Proceeding Congress on ADA in Aerospace, Barcelona (1990)

    Google Scholar 

  9. Chesneaux, J.M., Vignes, J.: Sur la robustesse de la methode CESTAC. C.R. Acad. Sci. Paris Ser. I Math. 307, 855–860 (1988)

    MATH  Google Scholar 

  10. Dehghan, M., Hashemi, B., Ghatee, R.: Solution of the fully fuzzy linear systems using iterative techniques. Chaos Solutions Fractals 34, 316–336 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)

    MATH  Google Scholar 

  12. Ezzati, R., Khzerloo, S., Mahdavi, N., Valizadeh, Z.: New models and algorithms for solutions of single-signed fully fuzzy LR linear systems. Fuzzy Syst. 9, 1–26 (2012)

    MathSciNet  MATH  Google Scholar 

  13. Araghi, M.A.F., Fattahi, H.: Solving fuzzy linear systems in the stochastic arithmetic by applying CADNA library. In: The International Conference on Evolutionary Computation Theory and Applications, pp. 446–450 (2011)

    Google Scholar 

  14. Araghi, M.A.F., Barzegar, H.: Dynamical control of accuracy in the fuzzy Runge-Kutta methods to estimate the solution of a fuzzy differential equation. In: Fuzzy Set Valued Analysis, pp. 71–84 (2016)

    Google Scholar 

  15. Araghi, M.A.F.: The methods of valid implementation of the numerical algorithms. Ph.d. dissertation thesis, science and research branch, Islamic Azad University, Tehran (2002)

    Google Scholar 

  16. Friedman, M., Ma, M., Kandel, A.: Fuzzy linear systems. Fuzzy Sets Syst. 96, 201–209 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  17. Noeiaghdam, S., Araghi, M.A.F.: Dynamical control of computations using the Gauss-Laguerre integration rule by applying the CADNA library. Adv. Appl. Math. Sci. 16, 1–18 (2016)

    Google Scholar 

  18. Ghanbari, R.: Solutions of fuzzy LR algebraic linear systems using linear programs. Appl. Math. Model. 39, 5164–5173 (2015)

    Article  MathSciNet  Google Scholar 

  19. Salkuyeh, D.K., Toutounian, F.: Numerical accuracy of a certain class of iterative methods for solving linear system. Appl. Math. Comp. 176, 727–738 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ortega, J.M.: Numerical Analysis: A Second Course. SIAM, Philadelphia (1990)

    Book  MATH  Google Scholar 

  21. Otadi, M., Mosleh, M.: Iterative method for approximate solution of fuzzy integro-differential equations. Beni-Suef Univ. J. Basic Appl. Sci. 5, 369–376 (2016)

    Article  Google Scholar 

  22. Saberi Najafi, H., Edalatpanah, S.A., Dutta, H.: A nonlinear model for fully fuzzy linear programming with fully unrestricted variables and parameters. Alexandria Eng. J. 55, 2589–2595 (2016)

    Article  Google Scholar 

  23. Siminski, K.: Interval type-2 neuro-fuzzy system with implication-based inference. Expert Syst. Appl. 79, 140–152 (2017)

    Article  Google Scholar 

  24. Vignes, J.: A stochastic arithmetic for reliable scientific computation. Math. Comput. Simul. 35, 233–261 (1993)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Ali Fariborzi Araghi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Fariborzi Araghi, M.A., Zarei, E. (2018). Dynamical Control of Computations Using the Iterative Methods to Solve Fully Fuzzy Linear Systems. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-66830-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66830-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66829-1

  • Online ISBN: 978-3-319-66830-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics