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Abstract
The F-transform is an effective tool in numerical mathematics that simplifies problem solving by transforming problems from the space of continuous functions into a finite-dimensional vector space. The aim of this study is to investigate the application of the F-transform in the numerical solution of second-order ordinary differential equations. In this work, the F-transform method is applied to solve initial value problems, two-point boundary value problems, and singular second-order differential equations. The research methodology is based on the development of numerical algorithms for approximating the solutions of linear and nonlinear second-order problems, along with the use of the inverse transform to obtain approximate solutions. The results demonstrate that, compared to classical methods such as the finite difference method, the proposed approach exhibits satisfactory accuracy and stability. Moreover, the convergence of F-transform-based methods is proven, and their efficiency is confirmed.
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References
- Butcher, J. C. (2003). Numerical Methods for Ordinary Differential Equations. Wiley online libruary. https://doi.org/978-0-471-96758-3
- Chaldeeva E. Perfilieva, I. (2008). Fuzzy transform in the analysis of data. International Journal of Approximate Reasoning. https://doi.org/10.1016/j.ijar.2007.06.003
- Chen, T. P. (2001). Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. Applied Mechanics Reviews . https://doi.org/10.1115/1.1421114
- Currenti Negro C. Coco, A. (2014). A Second Order Finite-Difference Ghost-Point Method for Elasticity Problems on Unbounded Domains with Applications to Volcanology. Communications in Computational Physics, 16(4), 983-1009. https://doi.org/10.4208/cicp.210713.010414a
- Di Martino, F. L. (2010). Fuzzy Transforms for Compression and Decompression of Colour Videos. Information Sciences, 180, 3914-3931. https://doi.org/https://doi.org/10.1016/j.ins.2010.06.030
- Dvorak A Perfilieva I, N. V. (2008). Fuzzy transform in the analysis of data. International Journal of Approximate Reasoning, 48(1), 36-46. https://doi.org/https://doi.org/10.1016/j.ijar.2007.06.003
- Iyengar S.R.K. Jain R.K Jain, M. (2014). Numerical methods for scientific and engineering. New Age International Publication.
- Jha G.j Verma A.K., K. S. (2019). A note on the convergence of fuzzy transformed finite difference methods. Journal of Applied Mathematics and Computing. https://doi.org/10.1007/s12190-019-01312-8
- Martino., V. L. (2009). A segmentation method for images compressed by fuzzy transforms. Fuzzy Sets and Systems, 161(1), 56-74. https://doi.org/https://doi.org/10.1016/j.fss.2009.08.002
- Penney, C. H. (2015). differential equations and boundary value problems. Pearson. https://doi.org/https://dl.konkur.in/post/Book/Paye/Differential-Equations-and-Boundary-Value-Problems-Edwards-5th-Edition-%5Bkonkur.in%5D.pdf
- Perfilieva, I. (2006). Fuzzy transforms: Theory and applications. Fuzzy Sets and Systems, 157(8), 993-1023. https://doi.org/https://doi.org/10.1016/j.fss.2005.11.012
- Perfilieva, I. C. (2001). Fuzzy transformation and its applications. Japan Seminar on Data Analysis and Decision Making under Uncertainity,pp.116–124(2001. https://utia.cas.cz/cs/news/3788/
- Perfilieva, I., & Kreinovich, V. (2011). Fuzzy transforms of higher order approximate derivatives: a theorem. Fuzzy sets and systems, 180(1), 55-68. https://doi.org/https://doi.org/10.1016/j.fss.2011.05.005
- Perfilieva, I., Danková, M., & Bede, B. (2011). Towards a higher degree f-transform. Fuzzy Sets Syst., 180(1), 3-19. https://doi.org/https://doi.org/10.1016/j.fss.2010.11.002
- Perfiliva, I., Demicco, R. V., & Klir, G. J. (2004). Fuzzy logic in geology. Academic Press, Burlington. https://doi.org/https://books.google.com.af/books?id=BwZUQ-Vz1sUC&printsec=copyright#v=onepage&q&f=false
- R.L.Burden, J. (2013). Numerical analysis. CENGAGE learing.
- R.Valasek I.perfilieva, P. (2017). F-transform-based shooting method for nonlinear boundary value problems. Soft Computing, 21(13), 1-10. https://doi.org/10.1007/s00500-017-2543-5
- Stepnicka, M., & Valásek, R. (2004). Fuzzy transforms and their application on wave equation. Electrical Engineering, 7, 55. https://doi.org/https://www.researchgate.net/publication/265358007_Fuzzy_transforms_and_their_application_to_wave_equation
- Stepnicka, M., & Valásek, R. (2005). .: Numerical solution of partial differential equations with help of fuzzy. The 14th IEEE International Conference on Fuzzy Systems.
- V.Novak I. Perfilieva, A. (2016). Insight in to fuzzy modeling. wiley online libruary. https://doi.org/10.1002/9781119193210.index
- Val´aˇsek., I. P. (2004). Fuzzy Transforms in Removing Noise. Computational Intelligence, Theory and Applications. Dortmund, Germany,: Computational Intelligence, Theory and Applications. https://doi.org/10.1007/3-540-31182-3_19
- Vilum.Novak, I, p., & A, D. (2016). Insight in to fuzzy modelling. wiley.
- Wei Chen, Y. S. (2014). Approximate solution for a class of second-order ordinary differential equations by the fuzzy transform. Journal of Intelligent & Fuzzy Systems 27(1):73-82. https://doi.org/10.3233/IFS-130979
- Zeinali, M., Alikhani, R., Shahmorad, S., Bahrami, F., & Perfilieva, I. (2018). On the structural properties of. Fuzzy set and systems, 342, 32-52. https://doi.org/https://doi.org/10.1016/j.fss.2017.12.008
References
Butcher, J. C. (2003). Numerical Methods for Ordinary Differential Equations. Wiley online libruary. https://doi.org/978-0-471-96758-3
Chaldeeva E. Perfilieva, I. (2008). Fuzzy transform in the analysis of data. International Journal of Approximate Reasoning. https://doi.org/10.1016/j.ijar.2007.06.003
Chen, T. P. (2001). Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. Applied Mechanics Reviews . https://doi.org/10.1115/1.1421114
Currenti Negro C. Coco, A. (2014). A Second Order Finite-Difference Ghost-Point Method for Elasticity Problems on Unbounded Domains with Applications to Volcanology. Communications in Computational Physics, 16(4), 983-1009. https://doi.org/10.4208/cicp.210713.010414a
Di Martino, F. L. (2010). Fuzzy Transforms for Compression and Decompression of Colour Videos. Information Sciences, 180, 3914-3931. https://doi.org/https://doi.org/10.1016/j.ins.2010.06.030
Dvorak A Perfilieva I, N. V. (2008). Fuzzy transform in the analysis of data. International Journal of Approximate Reasoning, 48(1), 36-46. https://doi.org/https://doi.org/10.1016/j.ijar.2007.06.003
Iyengar S.R.K. Jain R.K Jain, M. (2014). Numerical methods for scientific and engineering. New Age International Publication.
Jha G.j Verma A.K., K. S. (2019). A note on the convergence of fuzzy transformed finite difference methods. Journal of Applied Mathematics and Computing. https://doi.org/10.1007/s12190-019-01312-8
Martino., V. L. (2009). A segmentation method for images compressed by fuzzy transforms. Fuzzy Sets and Systems, 161(1), 56-74. https://doi.org/https://doi.org/10.1016/j.fss.2009.08.002
Penney, C. H. (2015). differential equations and boundary value problems. Pearson. https://doi.org/https://dl.konkur.in/post/Book/Paye/Differential-Equations-and-Boundary-Value-Problems-Edwards-5th-Edition-%5Bkonkur.in%5D.pdf
Perfilieva, I. (2006). Fuzzy transforms: Theory and applications. Fuzzy Sets and Systems, 157(8), 993-1023. https://doi.org/https://doi.org/10.1016/j.fss.2005.11.012
Perfilieva, I. C. (2001). Fuzzy transformation and its applications. Japan Seminar on Data Analysis and Decision Making under Uncertainity,pp.116–124(2001. https://utia.cas.cz/cs/news/3788/
Perfilieva, I., & Kreinovich, V. (2011). Fuzzy transforms of higher order approximate derivatives: a theorem. Fuzzy sets and systems, 180(1), 55-68. https://doi.org/https://doi.org/10.1016/j.fss.2011.05.005
Perfilieva, I., Danková, M., & Bede, B. (2011). Towards a higher degree f-transform. Fuzzy Sets Syst., 180(1), 3-19. https://doi.org/https://doi.org/10.1016/j.fss.2010.11.002
Perfiliva, I., Demicco, R. V., & Klir, G. J. (2004). Fuzzy logic in geology. Academic Press, Burlington. https://doi.org/https://books.google.com.af/books?id=BwZUQ-Vz1sUC&printsec=copyright#v=onepage&q&f=false
R.L.Burden, J. (2013). Numerical analysis. CENGAGE learing.
R.Valasek I.perfilieva, P. (2017). F-transform-based shooting method for nonlinear boundary value problems. Soft Computing, 21(13), 1-10. https://doi.org/10.1007/s00500-017-2543-5
Stepnicka, M., & Valásek, R. (2004). Fuzzy transforms and their application on wave equation. Electrical Engineering, 7, 55. https://doi.org/https://www.researchgate.net/publication/265358007_Fuzzy_transforms_and_their_application_to_wave_equation
Stepnicka, M., & Valásek, R. (2005). .: Numerical solution of partial differential equations with help of fuzzy. The 14th IEEE International Conference on Fuzzy Systems.
V.Novak I. Perfilieva, A. (2016). Insight in to fuzzy modeling. wiley online libruary. https://doi.org/10.1002/9781119193210.index
Val´aˇsek., I. P. (2004). Fuzzy Transforms in Removing Noise. Computational Intelligence, Theory and Applications. Dortmund, Germany,: Computational Intelligence, Theory and Applications. https://doi.org/10.1007/3-540-31182-3_19
Vilum.Novak, I, p., & A, D. (2016). Insight in to fuzzy modelling. wiley.
Wei Chen, Y. S. (2014). Approximate solution for a class of second-order ordinary differential equations by the fuzzy transform. Journal of Intelligent & Fuzzy Systems 27(1):73-82. https://doi.org/10.3233/IFS-130979
Zeinali, M., Alikhani, R., Shahmorad, S., Bahrami, F., & Perfilieva, I. (2018). On the structural properties of. Fuzzy set and systems, 342, 32-52. https://doi.org/https://doi.org/10.1016/j.fss.2017.12.008