Riemann–Liouville fractional integral of non-affine fractal interpolation function and its fractional operator

Author(s):  
T. M. C. Priyanka ◽  
A. Gowrisankar
Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750019 ◽  
Author(s):  
SRIJANANI ANURAG PRASAD

Riemann–Liouville fractional calculus of Coalescence Hidden-variable Fractal Interpolation Function (CHFIF) is studied in this paper. It is shown in this paper that fractional integral of order [Formula: see text] of a CHFIF defined on any interval [Formula: see text] is also a CHFIF albeit passing through different interpolation points. Further, conditions for fractional derivative of order [Formula: see text] of a CHFIF is derived in this paper. It is shown that under these conditions on free parameters, fractional derivative of order [Formula: see text] of a CHFIF defined on any interval [Formula: see text] is also a CHFIF.


Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 767
Author(s):  
Alexandra Băicoianu ◽  
Cristina Maria Păcurar ◽  
Marius Păun

The present paper concretizes the models proposed by S. Ri and N. Secelean. S. Ri proposed the construction of the fractal interpolation function(FIF) considering finite systems consisting of Rakotch contractions, but produced no concretization of the model. N. Secelean considered countable systems of Banach contractions to produce the fractal interpolation function. Based on the abovementioned results, in this paper, we propose two different algorithms to produce the fractal interpolation functions both in the affine and non-affine cases. The theoretical context we were working in suppose a countable set of starting points and a countable system of Rakotch contractions. Due to the computational restrictions, the algorithms constructed in the applications have the weakness that they use a finite set of starting points and a finite system of Rakotch contractions. In this respect, the attractor obtained is a two-step approximation. The large number of points used in the computations and the graphical results lead us to the conclusion that the attractor obtained is a good approximation of the fractal interpolation function in both cases, affine and non-affine FIFs. In this way, we also provide a concretization of the scheme presented by C.M. Păcurar .


2021 ◽  
Author(s):  
Easwaramoorthy D. ◽  
Gowrisankar A. ◽  
Manimaran A. ◽  
Nandhini S. ◽  
Santo Banerjee ◽  
...  

Abstract The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the dataset of the number of deaths per day and the number of infected cases per day of the most affected countries, The United States of America, Brazil, Russia, India, and The United Kingdom have been considered in first and second wave. The correlation fractal dimension has been estimated for the prescribed datasets of COVID-19 and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to picturize the transmission rate in the both times.


Fractals ◽  
2005 ◽  
Vol 13 (01) ◽  
pp. 33-41 ◽  
Author(s):  
ZHIGANG FENG ◽  
LIXIN TIAN ◽  
JIANLI JIAO

Fractal interpolation function (FIF) is continuous on its interval of definition. As a special kind of continuous function, FIFs' integrations on various scales and Fourier transform are studied in this paper. All of them can be expressed by the parameters of the corresponding iterative function systems.


2021 ◽  
Vol 5 (4) ◽  
pp. 185
Author(s):  
Kshitij Kumar Pandey ◽  
Puthan Veedu Viswanathan

There has been a considerable evolution of the theory of fractal interpolation function (FIF) over the last three decades. Recently, we introduced a multivariate analogue of a special class of FIFs, which is referred to as α-fractal functions, from the viewpoint of approximation theory. In the current note, we continue our study on multivariate α-fractal functions, but in the context of a few complete function spaces. For a class of fractal functions defined on a hyperrectangle Ω in the Euclidean space Rn, we derive conditions on the defining parameters so that the fractal functions are elements of some standard function spaces such as the Lebesgue spaces Lp(Ω), Sobolev spaces Wm,p(Ω), and Hölder spaces Cm,σ(Ω), which are Banach spaces. As a simple consequence, for some special choices of the parameters, we provide bounds for the Hausdorff dimension of the graph of the corresponding multivariate α-fractal function. We shall also hint at an associated notion of fractal operator that maps each multivariate function in one of these function spaces to its fractal counterpart. The latter part of this note establishes that the Riemann–Liouville fractional integral of a continuous multivariate α-fractal function is a fractal function of similar kind.


2011 ◽  
Vol 27 (1) ◽  
Author(s):  
Nicolae-Adrian Secelean

In this paper we will show that, if a countable interpolation data depends continuously on a parameter and some proper continuity conditions are fulfilled, then its associated attractor and the corresponding countable fractal interpolation function depends also continuously on the respective parameter. An example in R^2 is given.


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