Research article

On the construction of recurrent fractal interpolation functions using Geraghty contractions

  • Received: 21 July 2023 Revised: 22 September 2023 Accepted: 07 October 2023 Published: 26 October 2023
  • The recurrent iterated function systems (RIFS) were first introduced by Barnsley and Demko and generalized the usual iterated function systems (IFS). This new method allowed the construction of more general sets, which do not have to exhibit the strict self similarity of the IFS case and, in particular, the construction of recurrent fractal interpolation functions (RFIF). Given a data set $ \{ (x_n, y_n) \in I\times \mathbb R, n = 0, 1, \ldots, N \} $ where $ I = [x_0, x_N] $, we ensured that attractors of RIFS constructed using Geraghty contractions were graphs of some continuous functions which interpolated the given data. Our approach goes beyond the classical framework and provided a wide variety of systems for different approximations problems and, thus, gives more flexibility and applicability of the fractal interpolation method. As an application, we studied the error rates of time series related to the vaccination of COVID-19 using RFIF, and we compared them with the obtained results on the FIF.

    Citation: Najmeddine Attia, Hajer Jebali. On the construction of recurrent fractal interpolation functions using Geraghty contractions[J]. Electronic Research Archive, 2023, 31(11): 6866-6880. doi: 10.3934/era.2023347

    Related Papers:

  • The recurrent iterated function systems (RIFS) were first introduced by Barnsley and Demko and generalized the usual iterated function systems (IFS). This new method allowed the construction of more general sets, which do not have to exhibit the strict self similarity of the IFS case and, in particular, the construction of recurrent fractal interpolation functions (RFIF). Given a data set $ \{ (x_n, y_n) \in I\times \mathbb R, n = 0, 1, \ldots, N \} $ where $ I = [x_0, x_N] $, we ensured that attractors of RIFS constructed using Geraghty contractions were graphs of some continuous functions which interpolated the given data. Our approach goes beyond the classical framework and provided a wide variety of systems for different approximations problems and, thus, gives more flexibility and applicability of the fractal interpolation method. As an application, we studied the error rates of time series related to the vaccination of COVID-19 using RFIF, and we compared them with the obtained results on the FIF.



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