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Numerical Solution of Fuzzy Stochastic Volterra-Fredholm Integral Equation with Imprecisely Defined Parameters

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Recent Trends in Wave Mechanics and Vibrations

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Uncertainties play a major role in stochastic mechanics problems. To study the trajectory involved in stochastic mechanics problems generally, probability distributions are considered. Accordingly, the stochastic mechanics problems govern by stochastic differential equations followed by Markov process. However, the observation still lacks some sort of uncertainties, which are important but ignored. These imprecise uncertainties involved in the various factors affecting the constants, coefficients, initial, and boundary conditions. Hence, there may be a possibility to model a more reliable strategy that will quantify the uncertainty with better confidence. In this context, a computational method for solving fuzzy stochastic Volterra-Fredholm integral equation, which is based on the Block Pulse Functions (BPFs) using fuzzy stochastic operational matrix, is presented. The developed model is used to investigate a test problem of fuzzy stochastic Volterra integral equation and the results are compared in special cases.

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References

  1. Arqub OA (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm-Volterra integrodifferential equations. Neural Comput Appl 28:1591–1610

    Article  Google Scholar 

  2. Arqub OA, Al-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20:3283–3302

    Article  Google Scholar 

  3. Arqub OA, Al-Smadi M, Momani S, Hayat T (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21:7191–7206

    Article  Google Scholar 

  4. Babolian E, Maleknejad K, Mordad M, Rahimi B (2011) A numerical method to solve Fredholm-Volterra integral equations in two dimensional spaces using block pulse functions and operational matrix. J Comput Appl Math 235(14):3965–3971

    Article  MathSciNet  Google Scholar 

  5. Berger MA, Mizel VJ (1980) Volterra equations with Ito integrals I. J Integr Eqn 2:187–245

    MathSciNet  MATH  Google Scholar 

  6. Chakraverty S, Nayak S (2013) Fuzzy finite element method in diffusion problems. In: Mathematics of Uncertainty Modelling in the Analysis of Engineering and Science Problems. IGI global, pp 309–328

    Google Scholar 

  7. Cortes JC, Jodar L, Villafuerte L (2007) Numerical solution of random differential equations: a mean square approach. Math Comput Model 45:757–765

    Article  MathSciNet  Google Scholar 

  8. Etheridge A (2002) A course in financial calculus. Cambridge University Press

    Google Scholar 

  9. Jankovic S, Ilic D (2010) One linear analytic approximation for stochastic integro-differential eauations. Acta Mathematica Scientia 30(4):1073–1085

    Article  MathSciNet  Google Scholar 

  10. Jiang ZH, Schaufelberger W (1992) Block pulse functions and their applications in control systems. Springer

    Google Scholar 

  11. Khodabin M, Maleknejad K, Rostami M, Nouri M (2011) Numerical solution of stochastic differential equations by second order Runge-Kutta methods. Math Comput Model 53:1910–1920

    Article  MathSciNet  Google Scholar 

  12. Kloeden PE, Platen E (1999) Numerical solution of stochastic differential equations. In: Applications of mathematics. Springer, Berlin

    Google Scholar 

  13. Knight FH (2006) Risk, uncertainty and profit. Cosimo Classics, New York

    Google Scholar 

  14. Maleknejad K, Mahmoudi Y (2004) Numerical solution of linear Fredholm integral equation by using hybrid Taylor and block pulse functions. Appl Math Comput 149:799–806

    MathSciNet  MATH  Google Scholar 

  15. Maleknejad K, Shahrezaee M, Khatami H (2005) Numerical solution of integral equations system of the second kind by block pulse functions. Appl Math Comput 166:15–24

    MathSciNet  MATH  Google Scholar 

  16. Malinowski MT, Michta M (2011) Stochastic fuzzy differential equations with an application. Kybernetika 47(1):123–143

    MathSciNet  MATH  Google Scholar 

  17. Murge MG, Pachpatte BG (1990) Succesive approximations for solutions of second order stochastic integrodifferential equations of Ito type. Indian J Pure Appl Math 21(3):260–274

    MathSciNet  MATH  Google Scholar 

  18. Nayak S, Chakraverty S (2016) Numerical solution of stochastic point kinetic neutron diffusion equation with fuzzy parameters. Nucl Technol 193(3):444–456

    Article  Google Scholar 

  19. Nayak S, Chakraverty S (2016) Numerical solution of fuzzy stochastic differential equation. J Intell Fuzzy Syst 31:555–563

    Article  Google Scholar 

  20. Nayak S, Marwala T, Chakraverty S (2018) Stochastic differential equations with imprecisely defined parameters in market analysis. Soft Comput. https://doi.org/10.1007/s00500-018-3396-2

    Article  MATH  Google Scholar 

  21. Oksendal B (2003) Stochastic differential equations: an introduction with applications. Springer, Heidelberg

    Book  Google Scholar 

  22. Prasada Rao G (1983) Piecewise constant orthogonal functions and their application to systems and control. Springer, Berlin

    Google Scholar 

  23. Tudor C, Tudor M (1995) Approximation schemes for Ito-Volterra stochastic equations. Boletin Sociedad Matemática Mexicana 3(1):73–85

    MathSciNet  MATH  Google Scholar 

  24. Yong J (2006) Backward stochastic Volterra integral equations and some related problems. Stoch Process Appl 116:779–795

    Article  MathSciNet  Google Scholar 

  25. Zhang X (2008) Euler schemes and large deviations for stochastic Volterra equations with singular kernels. J Diff Equat 244:2226–2250

    Article  MathSciNet  Google Scholar 

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Correspondence to Sukanta Nayak .

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Nayak, S. (2020). Numerical Solution of Fuzzy Stochastic Volterra-Fredholm Integral Equation with Imprecisely Defined Parameters. In: Chakraverty, S., Biswas, P. (eds) Recent Trends in Wave Mechanics and Vibrations. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0287-3_9

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  • DOI: https://doi.org/10.1007/978-981-15-0287-3_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0286-6

  • Online ISBN: 978-981-15-0287-3

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