
The objective of this article is to investigate a coupled implicit Caputo fractional p-Laplacian system, depending on boundary conditions of integral type, by the substitution method. The Avery-Peterson fixed point theorem is utilized for finding at least three solutions of the proposed coupled system. Furthermore, different types of Ulam stability, i.e., Hyers-Ulam stability, generalized Hyers-Ulam stability, Hyers-Ulam-Rassias stability and generalized Hyers-Ulam-Rassias stability, are achieved. Finally, an example is provided to authenticate the theoretical result.
Citation: Dongming Nie, Usman Riaz, Sumbel Begum, Akbar Zada. A coupled system of p-Laplacian implicit fractional differential equations depending on boundary conditions of integral type[J]. AIMS Mathematics, 2023, 8(7): 16417-16445. doi: 10.3934/math.2023839
[1] | Pinhong Long, Huo Tang, Wenshuai Wang . Functional inequalities for several classes of q-starlike and q-convex type analytic and multivalent functions using a generalized Bernardi integral operator. AIMS Mathematics, 2021, 6(2): 1191-1208. doi: 10.3934/math.2021073 |
[2] | Suriyakamol Thongjob, Kamsing Nonlaopon, Sortiris K. Ntouyas . Some (p, q)-Hardy type inequalities for (p, q)-integrable functions. AIMS Mathematics, 2021, 6(1): 77-89. doi: 10.3934/math.2021006 |
[3] | Muhammad Sabil Ur Rehman, Qazi Zahoor Ahmad, H. M. Srivastava, Nazar Khan, Maslina Darus, Bilal Khan . Applications of higher-order q-derivatives to the subclass of q-starlike functions associated with the Janowski functions. AIMS Mathematics, 2021, 6(2): 1110-1125. doi: 10.3934/math.2021067 |
[4] | M. Emin Özdemir, Saad I. Butt, Bahtiyar Bayraktar, Jamshed Nasir . Several integral inequalities for (α, s,m)-convex functions. AIMS Mathematics, 2020, 5(4): 3906-3921. doi: 10.3934/math.2020253 |
[5] | Chunhong Li, Dandan Yang, Chuanzhi Bai . Some Opial type inequalities in (p, q)-calculus. AIMS Mathematics, 2020, 5(6): 5893-5902. doi: 10.3934/math.2020377 |
[6] | Khadeejah Rasheed Alhindi, Khalid M. K. Alshammari, Huda Ali Aldweby . Classes of analytic functions involving the q-Ruschweyh operator and q-Bernardi operator. AIMS Mathematics, 2024, 9(11): 33301-33313. doi: 10.3934/math.20241589 |
[7] | Muhammad Amer Latif, Mehmet Kunt, Sever Silvestru Dragomir, İmdat İşcan . Post-quantum trapezoid type inequalities. AIMS Mathematics, 2020, 5(4): 4011-4026. doi: 10.3934/math.2020258 |
[8] | Arslan Hojat Ansari, Sumit Chandok, Liliana Guran, Shahrokh Farhadabadi, Dong Yun Shin, Choonkil Park . (F, h)-upper class type functions for cyclic admissible contractions in metric spaces. AIMS Mathematics, 2020, 5(5): 4853-4873. doi: 10.3934/math.2020310 |
[9] | Muhammad Uzair Awan, Nousheen Akhtar, Artion Kashuri, Muhammad Aslam Noor, Yu-Ming Chu . 2D approximately reciprocal ρ-convex functions and associated integral inequalities. AIMS Mathematics, 2020, 5(5): 4662-4680. doi: 10.3934/math.2020299 |
[10] | Georgia Irina Oros, Gheorghe Oros, Daniela Andrada Bardac-Vlada . Certain geometric properties of the fractional integral of the Bessel function of the first kind. AIMS Mathematics, 2024, 9(3): 7095-7110. doi: 10.3934/math.2024346 |
The objective of this article is to investigate a coupled implicit Caputo fractional p-Laplacian system, depending on boundary conditions of integral type, by the substitution method. The Avery-Peterson fixed point theorem is utilized for finding at least three solutions of the proposed coupled system. Furthermore, different types of Ulam stability, i.e., Hyers-Ulam stability, generalized Hyers-Ulam stability, Hyers-Ulam-Rassias stability and generalized Hyers-Ulam-Rassias stability, are achieved. Finally, an example is provided to authenticate the theoretical result.
In 1986, Barnsley [1] introduced the concept of the fractal interpolation functions (FIFs) as continuous functions interpolating a given set of data points. Following this, an iterative process for interpolating data that sat on a discrete closed subset of Rn was introduced by Dubuc [2,3] and a general construction was given by Massopust [4]. This construction is based on the Read-Bajraktarevic operator for the FIFs. More precisely, FIFs are generated by an iterated function system (IFS) and arise as fixed points of the Read-Bajraktarevic operator defined on suitable function space. Several authors were interested in the study of many significant properties of FIFs including calculus, dimension, smoothness, stability, perturbation error, and more ([5,6,7]). The problem of the existence of FIF returns to the study of the existence and uniqueness of some fixed points on the fractal space, and the most widely studied FIFs are based on the Banach fixed point theorem. Recently, many researchers have studied the existence of FIFs by using different well-known results obtained in the fixed point theory, like Rakotch and Matkowski's theorems [9,10,11], Geraghty's theorem [12] and so on.
Fractal interpolation has found applications in the field of image segmentation, image analysis, image compression and signal processing ([13,14]). In particular, uniformly sampled signal is required for the discrete wavelet transform computation when we consider the multifractal analysis to study the heart rate variability [15]. In order to gain more flexibility in natural-shape generation or in image compression, a new class of IFSs were introduced and were called RIFS [16,17]. The RIFS use elements of stochastic processes, which can provide more flexible methods since they involve local IFS [18,19]. It induces recurrent fractal interpolation functions (RFIFs) which main advantage is that they are not self-similar and they reflect only local similarities in contrast to the FIFs induced by the standard IFSs. Some properties of RFIFs, including statistical properties [20,21] and box dimension [22], have been studied (see also [17,23]). However, only RFIF, whose existence is ensured by the Banach fixed point theorem, has been considered.
In this paper, we aim to construct non-affine RFIFs using Geraghty contractions. Therefore, we present in the next section some results on RIFS and we give new conditions on the existence of the attractor of a new class of RIFS. These results will be explored in the third paragraph to define a new class of RFIF using Gheraghty contractions. Section four will be devoted to the study of the normalized root mean square errors of time series related to the vaccination of COVID-19 using RFIF, and we compare them with the obtained results on the FIF.
Let (X,d) be a complete, locally compact metric space and wj:X→X, for j=1,…,n are self-mappings on X. We define P=(pij) to be an irreducible n×n row stochastic matrix associated to these maps, i.e.,
n∑j=1pij=1, for all i=1,…,n and pij≥0, for all 1≤i,j≤n, | (2.1) |
and for any 1≤i,j≤n, there exists a sequence i1, i2,…,ik with i1=i and ik=j such that
pi1i2pi2i3…pik−1ik>0. | (2.2) |
A RIFS consists of an IFS (X,wi,i=1,…,n) together with a matrix P as defined above. They have their origins in the work of Barnsley and Demko [24] and have been developed by Barnsley et al. [17]. In fact, they are a natural generalization of the IFS that permits them to construct fractals as fixed points of some operators on complete metric spaces [16,25].
A RIFS is, in essence, a Markov chain with n states. Given an initial set that is transformed through the mapping wi, we should say that the chain is in state i. The value pij is the transition probability from state i to state j. That is, assuming that the mapping wi has been employed at the previous iteration, the probability of selecting the map wj for the next iteration is given by pij. The motivation underlying the necessary properties, that matrix (pij) should satisfy becomes apparent as a consequence of the Markov chain rationale. The first condition given by (2.1) ensures that the chain must be stochastic, that is, at every state the transition probabilities to any other state should sum up to one. The second one (2.2) means that the chain is irreducible. Namely, that we may transfer from any state of the chain to any other state. Therefore, we can be sure that, given sufficient time, all the mappings of the RIFS will be used. Otherwise, if P is reducible, it is possible that some of the mappings of the RIFS will be rendered useless.
Remark 1. An IFS with probabilities {X,w1,…,wn,p1,…,pn} [16] provides a simple example of an RIFS {X,w1,…,wn,(pij)1≤i,j≤n} when pij=pj, for all i,j∈{1,…,n}. In addition, if p1=p2=⋯=pn:=p, then the RIFS reduces to the usual IFS denoted simply by (X,w,p). Therefore, RIFSs provide more flexibility so that their attractors need not exhibit the self-similarity or self-tiling properties characteristic of IFS attractors.
Let H be the set of all nonempty compact subsets of X. We define the Hausdorff metric h by
h(A,B)=max{d(A,B),d(B,A)},A,B∈H, |
with
d(A,B)=supx∈Ainfy∈Bd(x,y) and d(B,A)=supx∈Binfy∈Ad(x,y). |
Then [16], the space (H,h) is complete, and compact whenever X is compact. Let {X,w1,…,wn,(pij)1≤i,j≤n} be a RIFS and let Wij:H→H be defined as
Wij(A)={wi(A)if pji>0∅. |
Now, consider the space Hn=H×⋯×H endowed with the metric ˜h defined as
˜h((A1,…,An),(B1,…,Bn))=max1≤i≤nh(Ai,Bi) |
and let W:Hn→Hn be given by
W(A1A2⋮An)=(W11W12…W1nW21W22…W2n⋮⋮⋮Wn1Wn2…Wnn)(A1A2⋮An)=(W11(A1)∪W12(A2)∪…∪W1n(An)W21(A1)∪W22(A2)∪…∪W2n(An)⋮Wn1(A1)∪Wn2(A2)∪…∪Wnn(An)). |
Thus,
W(A1A2⋮An)=(⋃j∈I(1)w1(Aj)⋃j∈I(2)w2(Aj)⋮⋃j∈I(n)wn(Aj)), | (2.3) |
where I(i)={1≤j≤n pji>0}, for i=1,…,n.
Example 1. We consider I={[0,1]2,wi,1=1,…,4} the RIFS equipped with the mappings
w1(xy)=(1/2001/2)(xy)+(00),w2(xy)=(1/2001/2)(xy)+(050)w3(xy)=(1/2001/2)(xy)+(500),w4(xy)=(1/2001/2)(xy)+(5050). | (2.4) |
If we consider the transition matrix
P=(1/201/201/31/301/31/21/20001/201/2), |
then, the function W is defined by
W=(w1w1w1∅∅w2w2w2w3∅∅∅∅w4∅w4). |
Now, let A=(A1,A2,A3,A4)T∈H4 be an initial point and assume that Ai was generated by applying the mapping wi to an unknown set for each i=1,2,3,4, then W(A) is computed as
W(A1A2A3A4)=(w1(A1)∪w1(A2)∪w1(A3)w2(A2)∪w2(A3)∪w2(A4)w3(A1)w4(A2)∪w4(A4)). |
Therefore, only the maps wj with positive transition probabilities, i.e., pij>0 can be applied to the set Ai. In particular, only the map w3 is applied to A1.
Although the Banach fixed point theorem is the most useful tool for the existence (and uniqueness) of attractors of IFS, there are numerous results in the literature related to some generalizations of the Banach principle that give sufficient conditions for the existence of a contractive fixed point (see for example [26,27,28]).
Definition 1. 1) Let (X,ρ) be a metric space. If for some function φ:(0,∞)→(0,∞) and a self-map f, we have :
∀ x,y∈X,ρ(f(x),f(y))≤φ(ρ(x,y)), |
then we say that f is a φ-contraction.
2) If f is a φ-contraction for some function φ:(0,∞)→(0,∞) such that for any t>0, α(t)=φ(t)t<1 and the function t↦φ(t)t is nonincreasing (so-called Geraghty I) (or nondecreasing (Geraghty II), or continuous (Geraghty III)), then we call such a function a Geraghty contraction.
It is easy to see that each Banach contraction is a Geraghty contraction. It suffices to consider the function φ(t)=αt, for some 0≤α<1. Furthermore, the notions of Geraghty I and III contractions coincide when the metric space (X,ρ) is compact [28].
Theorem 1. [26] Let (X,ρ) be a complete metric space and f:X→X be a Geraghty contraction then there is a unique fixed point x∞∈X of f, and for each x∈X, limn→+∞fn(x)=x∞.
Theorem 2. Assume that wi:X→X, for i=1,…,n are Geraghty contractions of a RIFS, then there exists a unique set A=(A1,…,An), called the attractor of the RIFS, such that W(A)=A and
Ai=⋃j∈I(i)wi(Aj),for i=1,…,n. |
We identify A to the union of all Ai, that is, A=n⋃i=1Ai.
Proof. Let φ:(0,∞)→(0,∞) be a nondecreasing function satisfying φ(t)t<1 for all t>0, and t↦φ(t)t is nonincreasing (or nondecreasing, or continuous), such that
d(wi(x),wi(y))≤φ(d(x,y)),∀x,y∈X. |
Then, we have
h(W(A),W(B))=h(n⋃i=1wi(A),n⋃i=1wi(B))≤max1≤i≤nh(wi(A),wi(B))=max1≤i≤n(max(d(wi(A),wi(B)),d(wi(B),wi(A))))≤max(φ(d(A,B)),φ(d(B,A)))≤φ(h(A,B)). |
Hence, the Hutchinson operator W is a Geraghty contraction and has a unique fixed compact set (Theorem 2.1). Using (2.3), this means that there exists n compact subsets Ai, for i=1,…,n, such that A=(A1,…,An) and
Ai=⋃j∈I(i)wi(Aj). |
An RIFS {X,w1,…,wn,(pij)1≤i,j≤n} is said to satisfy the open set condition (OSC) if there exists bounded nonempty open sets U1,U2,…,Un with the property that
⋃j,pji>0wi(Uj)⊂Ui,wi(Uj)∩wi(Uk)=∅,for allki≠ljwithpjipkj>0. | (2.5) |
When the RIFS satisies the OSC, we can compute the Hausdorff dimension of the attractor generated by the system. In fact, the attractor's Hausdorff dimension reflects how space-filling or complex the attractor is. Calculating the Hausdorff dimension of the attractor is a common method of quantifying its complexity. The condition (2.5) is satisfied in the case of RFIF in section three. It prevents the interpolated curve or fractal from collapsing into itself or having degenerate, overlapping segments. It ensures that the points used for interpolation are well separated and distinct, allowing for the generation of a meaningful and nondegenerate fractal or curve.
Example 2. This example involves four transformations that take the square into itself. For 1≤i≤4, the range of wi is the quadrant of the square labeled i. If the transition probability pij is zero, no points on the attractor lie in the sub-quadrant labeled ij. While the structure of the attractor of the RIFS depends only on whether or not pij is nonzero and not on the values of the pijs, the coloring and texture of the image can be controlled through the probability values (see [16] for more details). To illustrate that, different choices for the transition probabilities are considered. Let us consider again the mappings wi,i=1,2,3,4 defined in Example 1. To construct the attractor, we apply one map of the IFS to a random selected point. Then, at each step, we apply a new map according to the probabilities of the transition of the matrix. In Figure 1 (a1, a2 and a3), we illustrate the attractors of RIFS equipped respectively with the following transition matrices :
P1=(01000121201301313120012),P2=(14141414141414140131313001212),P3=(01201201201214141414120120). |
Now, we consider ˜I={[0,1]2,˜wi,i=1,…,4}, the RIFS equipped with the mappings
˜wi(x,y)=(aix+bi,cix+y1+βy+fi), for i=1,…,4, | (2.6) |
where β=0.01. These mappings are Geraghty contractions since they satisfy
|cix1+y11+βy1−cix2−y21+βy2|≤|ci||x1−x2|+φ(|y1−y2|), for all y1,y2∈[0,1], |
with φ(t)=t1+βt (this fact can be proved using Lemma 1). The attractors of ˜I, equipped with the transition matrices P1, P2 and P3, are illustrated in Figure 1 (b1, b2 and b3), respectively.
For n≥2, let {(xi,yi)∈R2, i=0,…,n} be a given data set, where x0<x1<…<xn and yi∈[a,b] for all i=0,…,n. For i=1,…,n, we set I=[x0,xn] and Ii=[xi−1,xi]. For each interval Ii, we define Di=[xl(i),xr(i)], with l(i),r(i)∈{0,…,n} such that l(i)<r(i) and xr(i)−xl(i)>xi−xi−1. For each i=1,…,n, we consider Li:[xl(i),xr(i)]→[xi−1,xi] as the homeomorphism given by Li(x)=aix+bi and Fi:Di×R→R as continuous mappings satisfying
Fi(xl(i),yl(i))=yi−1andFi(xr(i),yr(i))=yi. | (3.1) |
Assume that Fi is k-Lipschitz with respect to the first variable and a Geraghty contraction with respect to the second variable; that is
|Fi(x,y)−Fi(x′,y′)|≤k|x−x′|+φ(|y−y′|), for all x,x′∈Di, and y,y′∈R, | (3.2) |
where k≥0 and φ:(0,∞)→(0,∞) is a nondecreasing function such that, for any t>0, α(t):=φ(t)t<1 and the function α is nonincreasing (or nondecreasing, or continuous). We define wi:Di×R→Ii×R by wi(x,y)=(Li(x),Fi(x,y)). Set I(i)={j∈{1,…,n} Ij⊂Di}.
The proof of the following lemma mimics that of [9, Theorem 3.7] and [12, Theorem 3.3].
Lemma 1. There exists a metric dθ in I×[a,b] equivalent to the Euclidean metric, such that for all i=1,…,n, the mappings wi defined above, are Geraghty contractions with respect to dθ.
Proof. We define a metric dθ on I×[a,b] by
dθ((x,y),(x′,y′)):=|x−x′|+θ|y−y′|, |
where θ is a real number that is specified in the sequel. Then, for all (x,y),(x′,y′)∈I×[a,b], we have:
dθ(wi(x,y),wi(x′,y′))=dθ((Li(x),Fi(x,y)),(Li(x′),Fi(x′,y′)))=|Li(x)−Li(x′)|+θ|Fi(x,y)−Fi(x′,y′)|≤|ai||x−x′|+θk|x−x′|+θφ(|y−y′|)=(|ai|+θk)|x−x′|+θφ(|y−y′|)=(|ai|+θk+θφ(|y−y′|)|x−x′|+|y−y′|)|x−x′|+θφ(|y−y′|)|x−x′|+|y−y′||y−y′|≤(|ai|+θk+θφ(|x−x′|+|y−y′|)|x−x′|+|y−y′|)|x−x′|+θφ(|x−x′|+|y−y′|)|x−x′|+|y−y′||y−y′|≤(|ai|+θk+θ)|x−x′|+θφ(|x−x′|+|y−y′|)|x−x′|+|y−y′||y−y′|≤max(a+θk+θ,φ(|x−x′|+|y−y′|)|x−x′|+|y−y′|)dθ((x,y),(x′,y′)), |
with a=max1≤i≤n|ai|. Set θ:=1−a2(k+1), then 0<θ<1 and 0<a+θk+θ<1. Moreover, for t>0, let β(t):=max(a+θk+θ,φ(t)t). Then, β:(0,∞)→[0,1) is a nonincreasing (or nondecreasing, or continuous), since t↦φ(t)t is a nonncreasing (or nondecreasing, or continuous). Furthermore, we obtain:
dθ(wi(x,y),wi(x′,y′))≤β(d((x,y),(x′,y′)))dθ((x,y),(x′,y′)), |
where d((x,y),(x′,y′)):=|x−x′|+|y−y′|. Since 0<θ<1, then, for all (x,y)≠(x′,y′), we have:
θ|x−x′|+θ|y−y′|≤|x−x′|+θ|y−y′|≤|x−x′|+|y−y′|. |
Thus,
dθ((x,y),(x′,y′))≤d((x,y),(x′,y′))≤θ−1dθ((x,y),(x′,y′)). |
If β:(0,∞)→[0,1) is nonncreasing, then
dθ(wi(x,y),wi(x′,y′))≤β(d((x,y),(x′,y′)))dθ((x,y),(x′,y′))≤β(dθ((x,y),(x′,y′)))dθ((x,y),(x′,y′)). |
If β:(0,∞)→[0,1) is nondecreasing, then
dθ(wi(x,y),wi(x′,y′))≤β(d((x,y),(x′,y′)))dθ((x,y),(x′,y′))≤β(θ−1dθ((x,y),(x′,y′)))dθ((x,y),(x′,y′))≤β′(dθ((x,y),(x′,y′)))dθ((x,y),(x′,y′)), |
where β′:(0,∞)→[0,1) is a nondecreasing function defined as β′(t):=β(θ−1t).
If β:(0,∞)→[0,1) is continuous, then
dθ(wi(x,y),wi(x′,y′))≤β(d((x,y),(x′,y′)))dθ((x,y),(x′,y′))≤β″(dθ((x,y),(x′,y′)))dθ((x,y),(x′,y′)), |
where β″:(0,∞)→[0,1) is a continuous function defined as β″(t):=maxx∈[t,θ−1t]β(x). Hence, wi are Geraghty contractions in (I×[a,b],dθ). Moreover, we have:
min(1,θ)√|x−x′|2+|y−y′|2≤min(1,θ)(|x−x′|+|y−y′|)≤dθ((x,y),(x′,y′))≤(1+θ)√|x−x′|2+|y−y′|2. |
That is, dθ is equivalent to the Euclidean metric on I×[a,b]. In particular, (I×[a,b],dθ) is a complete metric space.
Denote by C(I) the set of continuous functions f:I=[x0,xn]→[a,b]. Let C∗(I) be the set of continuous functions on I that pass through the interpolation points; that is
C∗(I)={f∈C(I) | f(xi)=yi, ∀ i=0,…,n}. |
Then, C(I) and C∗(I) are complete metric spaces with respect to the uniform metric ρ defined by
ρ(f1,f2)=maxx∈I|f1(x)−f2(x)|. |
Define the Read-Bajraktarevic operator T on C∗(I) by
Tf(x)=Fi(L−1i(x),f(L−1i(x))), for x∈Ii and i=1,…,n. |
It is easy to verify that Tf is continuous on I and that Tf(xi)=yi, for all i=0,…,n.
Our main result in this section is given by the following theorem. This extends [17, Corollaries 3.5] and [17, Corollaries 3.7] and the results given in [9,12] regarding the existence of FIF defined with Rakotch and Geraghty contractions.
Theorem 3. For n≥2, let {(xi,yi); i=0,…,n} be a given data set and let {Di×R,wi; i=1,…,n} be the RIFS constructed above. Then, there exists a unique function ˜f∈C(I) such that ˜f(xi)=yi, for all i=0,…,n. Moreover, let Gi be the graph of the restriction of ˜f to Ii, then G=(G1,…,Gn) is the attractor of the RIFS {Di×R,wi; i=1,…,n}, i.e.,
Gi=⋃j∈I(i)wi(Gj), for i=1,…,n. |
Furthermore, the RFIF ˜f satisfies the functional equation
˜f(x)=Fi(L−1i(x),˜f∘L−1i(x)), for all x∈Ii and i=1,…,n. |
Proof. Let's first notice that the RIFS {Di×R,wi; i=1,…,n} has a unique attractor by Lemma 1 and Theorem 2.1. Moreover, for f,g∈C∗(I), we have:
ρ(Tf,Tg)=maxx∈I|Tf(x)−Tg(x)|=max1≤i≤nx∈Ii|Tf(x)−Tg(x)|=max1≤i≤nx∈Ii|Fi(L−1i(x),f(L−1i(x)))−Fi(L−1i(x),g(L−1i(x)))|≤max1≤i≤nsupx∈Iiφ(|f(L−1i(x))−g(L−1i(x))|). |
Since, for each i∈{1,…,n} and every x∈Ii, we have:
φ(|f(L−1i(x))−g(L−1i(x))|)≤φ(maxx∈Ii|f(L−1i(x))−g(L−1i(x))|)≤φ(maxx∈I|f(x)−g(x)|)=φ(ρ(f,g)), |
then, we obtain :
ρ(Tf,Tg)≤φ(ρ(f,g)). |
Thus, T is a Geraghty contraction and has a unique fixed point ˜f∈C∗(I), i.e. there exists a unique continuous function ˜f that interpolates the points (xi,yi), i=0,…,n and satisfies
˜f(x)=Fi(L−1i(x),˜f∘L−1i(x)), x∈Ii, i=1,…,n. |
Moreover, let G be the graph of f and let Gi be the graph of the restriction of f to Ii, for i=1,…,n, then,
G=n⋃i=1Gi. |
Furthermore, we have:
Gi={(x,˜f(x)); x∈Ii}={(x,Fi(L−1i(x),˜f(L−1i(x)))) ;x∈Ii}={(Li(x),Fi(x,˜f(x)));x∈Di}={wi(x,˜f(x));x∈Di}. |
Hence, (G1,…,Gn) is the invariant set of W, i.e., the attractor of the RIFS {Di×R,wi; i=1,…,n}, and we have:
Gi=⋃j∈I(i)wi(Gj), for i=1,…,n. |
Example 3. Considering the interpolation data
{(0,9);(0.25,11);(0.5,10);(0.75,11);(1,8)}, |
we illustrate the RFIFs corresponding to ˜I equipped with the transition matrices P1, P2 and P3 given in Example 2 in Figure 2 (l1–l3), respectively. The subsequent figure (f1–f3) illustrates the RFIF equipped with affine transformations of Example 1 with the same respective transition matrices so that,
D1 | D2 | D3 | D4 | |
P1 | [12,1] | [0,12] | [14,34] | [12,1] |
P2 | [0,12] | [0,34] | [0,1] | [0,1] |
P3 | [12,1] | [0,34] | [12,1] | [0,34]. |
In this section, we will compare RFIF with the classical FIF technique in terms of error rates of the time-series representing the daily vaccinations data. We can show that the fractal interpolation method gives lower error rates than the cubic splines, which is often used to avoid the problem of Runge's phenomenon [29]. For this, we collect (from the website ourworldindata.org) daily vaccination data from the following four countries: Germany, Italy, Tunisia and France (see Table 1). As seen in Figure 3, the irregularities of this data is very visible and the reconstruction of the curve from the point of view of fractal interpolation is significantly relevant. This can be an effective way to recover missing information due to insufficient testing, and to analyze the complexity of the evolution of COVID-19 given the importance of vaccination data for each day. In addition, the variations of the FIF incorporate the behavior of the data (see Figure 4). Two methods of fractal interpolation namely linear interpolation, where
Fi(x,y)=αy+cix+di, |
as well as fractal interpolation defined by Geraghty contractions, with
Fi(x,y)=y1+βy+c′ix+d′i, |
are considered.
Tun | Fra | Ita | Ger | |
total data obtained | 273 | 669 | 671 | 670 |
interpolation data | 69 | 168 | 168 | 168 |
A common way of measuring the quality of the fit of the interpolation is the normalized root mean square error (NRMSE), which is a statistical error indicator that facilitates the comparison between datasets with different scales and ensures a more universal measure of comparison. There exists various different methods of RMSE normalizations, and we will use
NRMSE=√∑ni=0(yi−^yi)21n∑ni=0yi, |
where yi is the ith observation of y, ˆyi is the predicted y value given by the interpolation technique and n is the number of total data considered.
In Table 2, we present the results obtained for different parameters α and β concerning the daily vaccination data of France. The parameters α and β have been chosen arbitrarily. We can see that the RFIF is less sensitive to parameter change compared to the FIF which has big differences in the NMRSE for the various parameter values. This may be explained by the fact that other parameters are involved in the RFIF, namely, the coefficients of the stochastic matrix. Even though we did not get relevant information on the evolution of the disease, we can see the fractal structure of the epidemic curve. Moreover, the presence of parameters α and β help to get a wide variety of mappings for approximating problems. The obtained values show a slight improvement when the recurrent fractal interpolation is deployed. Nevertheless, there are still questions about optimal values of the entries of the stochastic matrix that can be managed to determine an optimal choice of the intervals Di (as defined in Section 3), as well as optimal choice of β (as given in [13,30] for the choice of the vertical scaling factor) so that the obtained curves fit as closely as possible to the real values.
Fractal interpolation | Recurrent fractal interpolation | ||
Fi(x,y)=αy+cix+di | α=0.1 | 273.819 | 264.536 |
α=0.2 | 310.896 | 270.743 | |
α=0.3 | 379.131 | 281.771 | |
α=0.5 | 636.935 | 320.554 | |
Fi(x,y)=y1+βy+c′ix+d′i | β=0.015 | 263.342 | 263.252 |
β=0.025 | 263.283 | 263.283 | |
β=0.045 | 263.354 | 263.299 | |
β=0.1 | 263.278 | 263.345 |
RIFS can be seen as improvements of the notion of IFS and were introduced in order to gain more flexibility in natural-shape generation and image compression. In this paper, we gave new conditions on the existence of the attractor of a new class of RIFS based on the condition of Geraghty contractions. Consequently, we constructed a new class of RFIF.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors extend their appreciation to the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research at King Faisal University, Saudi Arabia, for financial support under the annual funding track [GRANT 4413].
The authors declare no conflict of interest.
[1] |
R. Agarwal, Y. Zhou, Y. He, Existence of fractional neutral functional differential equations, Comput. Math. Appl., 59 (2010), 1095–1100. http://dx.doi.org/10.1016/j.camwa.2009.05.010 doi: 10.1016/j.camwa.2009.05.010
![]() |
[2] |
M. Ahmad, A. Zada, J. Alzabut, Stability analysis of a nonlinear coupled implicit switched singular fractional differential equations with p-Laplacian, Adv. Differ. Equ., 2019 (2019), 436. http://dx.doi.org/10.1186/s13662-019-2367-y doi: 10.1186/s13662-019-2367-y
![]() |
[3] |
M. Ahmad, A. Zada, J. Alzabut, Hyers-Ulam stability of a coupled system of fractional differential equations of Hilfer-Hadamard type, Demonstr. Math., 52 (2019), 283–295. http://dx.doi.org/10.1515/dema-2019-0024 doi: 10.1515/dema-2019-0024
![]() |
[4] |
Asma, J. Gomez-Aguilar, G. Rahman, M. Javed, Stability analysis for fractional order implicit ψ-Hilfer differential equations, Math. Method. Appl. Sci., 45 (2022), 2701–2712. http://dx.doi.org/10.1002/mma.7948 doi: 10.1002/mma.7948
![]() |
[5] |
R. Dhayal, J. Gomez-Aguilar, J. Torres-Jimenez, Stability analysis of Atangana-Baleanu fractional stochastic differential systems with impulses, Int. J. Syst. Sci., 53 (2022), 3481–3495. http://dx.doi.org/10.1080/00207721.2022.2090638 doi: 10.1080/00207721.2022.2090638
![]() |
[6] |
A. Gonzalez-Calderon, L. Vivas-Cruz, M. Taneco-Hernandez, J. Gomez-Aguilar, Assessment of the performance of the hyperbolic-NILT method to solve fractional differential equations, Math. Comput. Simulat., 206 (2023), 375–390. http://dx.doi.org/10.1016/j.matcom.2022.11.022 doi: 10.1016/j.matcom.2022.11.022
![]() |
[7] |
L. Guo, U. Riaz, A. Zada, M. Alam, On implicit coupled Hadamard fractional differential equations with generalized Hadamard fractional integro-differential boundary conditions, Fractal Fract., 7 (2023), 13. http://dx.doi.org/10.3390/fractalfract7010013 doi: 10.3390/fractalfract7010013
![]() |
[8] |
M. Iswarya, R. Raja, G. Rajchakit, J. Cao, J. Alzabut, C. Huang, Existence, uniqueness and exponential stability of periodic solution for discrete-time delayed BAM neural networks based on coincidence degree theory and graph theoretic method, Mathematics, 7 (2019), 1055. http://dx.doi.org/10.3390/math7111055 doi: 10.3390/math7111055
![]() |
[9] |
H. Khan, T. Abdeljawad, J. Gomez-Aguilar, H. Tajadodi, A. Khan, Fractional order Volterra integro-differential equation with Mittag-Leffler kernel, Fractals, 29 (2021), 2150154. http://dx.doi.org/10.1142/S0218348X21501541 doi: 10.1142/S0218348X21501541
![]() |
[10] | A. Kilbas, H. Srivastava, J. Trujillo, Theory and application of fractional differential equation, Amsterdam: Elsevier, 2006. |
[11] |
X. Liu, M. Jia, W. Ge, Multiple solutions of a p-Laplacian model involving fractional derivatives, Adv. Differ. Equ., 2013 (2013), 126. http://dx.doi.org/10.1186/1687-1847-2013-126 doi: 10.1186/1687-1847-2013-126
![]() |
[12] |
H. Lu, Z. Han, S. Sun, J. Liu, Existence on positive solutions for boundary value problems of nonlinear fractional differential equations with p-Laplacian, Adv. Differ. Equ., 2013 (2013), 30. http://dx.doi.org/10.1186/1687-1847-2013-30 doi: 10.1186/1687-1847-2013-30
![]() |
[13] |
O. Martinez-Fuentes, F. Melendez-Vazquez, G. Fernandez-Anaya, J. Gomez-Aguilar, Analysis of fractional-order nonlinear dynamic systems with general analytic kernels: lyapunov stability and inequalities, Mathematics, 9 (2021), 2084. http://dx.doi.org/10.3390/math9172084 doi: 10.3390/math9172084
![]() |
[14] | M. Matar, A. Lubbad, J. Alzabut, On p-Laplacian boundary value problems involving Caputo-Katugampula fractional derivatives, Math. Method. Appl. Sci., in press. http://dx.doi.org/10.1002/mma.6534 |
[15] | M. Obloza, Hyers stability of the linear differential equation, Rocznik NaukDydakt, Prace Mat., 13 (1993), 259–270. |
[16] |
K. Oldham, Fractional differential equations in electrochemistry, Adv. Eng. Softw., 41 (2010), 9–12. http://dx.doi.org/10.1016/j.advengsoft.2008.12.012 doi: 10.1016/j.advengsoft.2008.12.012
![]() |
[17] |
A. Pratap, R. Raja, J. Cao, J. Alzabut, C. Huang, Finite-time synchronization criterion of graph theory perspective fractional order coupled discontinuous neural networks, Adv. Differ. Equ., 2020 (2020), 97. http://dx.doi.org/10.1186/s13662-020-02551-x doi: 10.1186/s13662-020-02551-x
![]() |
[18] |
U. Riaz, A. Zada, Analysis of (α,β)-order coupled implicit Caputo fractional differential equations using topological degree method, Int. J. Nonlin. Sci. Num., 22 (2021), 897–915. http://dx.doi.org/10.1515/ijnsns-2020-0082 doi: 10.1515/ijnsns-2020-0082
![]() |
[19] |
U. Riaz, A. Zada, Z. Ali, Y. Cui, J. Xu, Analysis of coupled systems of implicit impulsive fractional differential equations involving Hadamard derivatives, Adv. Differ. Equ., 2019 (2019), 226. http://dx.doi.org/10.1186/s13662-019-2163-8 doi: 10.1186/s13662-019-2163-8
![]() |
[20] |
U. Riaz, A. Zada, Z. Ali, I. Popa, S. Rezapour, S. Etemad, On a Riemann-Liouville type implicit coupled system via generalized boundary conditions, Mathematics, 9 (2021), 1205. http://dx.doi.org/10.3390/math9111205 doi: 10.3390/math9111205
![]() |
[21] |
F. Rihan, Numerical modeling of fractional order biological systems, Abstr. Appl. Anal., 2013 (2013), 816803. http://dx.doi.org/10.1155/2013/816803 doi: 10.1155/2013/816803
![]() |
[22] | J. Sabatier, O. Agrawal, J. Machado, Advances in fractional calculus, Dordrecht: Springer, 2007. http://dx.doi.org/10.1007/978-1-4020-6042-7 |
[23] |
A. Seemab, M. Rehman, J. Alzabut, A. Hamdi, On the existence of positive solutions for generalized fractional boundary value problems, Bound. Value Probl., 2019 (2019), 186. http://dx.doi.org/10.1186/s13661-019-01300-8 doi: 10.1186/s13661-019-01300-8
![]() |
[24] | V. Tarasov, Fractional dynamics: application of fractional calculus to dynamics of particles, fields and media, Berlin: Springer, 2010. http://dx.doi.org/10.1007/978-3-642-14003-7 |
[25] | S. Ulam, A collection of the mathematical problems, New York: Interscience Publishers, 1960. |
[26] | B. Vintagre, I. Podlybni, A. Hernandez, V. Feliu, Some approximations of fractional order operators used in control theory and applications, Fract. Calc. Appl. Anal., 3 (2000), 231–248. |
[27] |
H. Waheed, A. Zada, R. Rizwan, I. Popa, Hyers-Ulam stability for a coupled system of fractional differential equation with p-Laplacian operator having integral boundary conditions, Qual. Theory Dyn. Syst., 21 (2022), 92. http://dx.doi.org/10.1007/s12346-022-00624-8 doi: 10.1007/s12346-022-00624-8
![]() |
[28] |
H. Yepez-Martinez, J. Gomez-Aguilar, M. Inc, New modified Atangana-Baleanu fractional derivative applied to solve nonlinear fractional differential equations, Phys. Scr., 98 (2023), 035202. http://dx.doi.org/10.1088/1402-4896/acb591 doi: 10.1088/1402-4896/acb591
![]() |
[29] |
A. Zada, J. Alzabut, H. Waheed, I. Popa, Ulam-Hyers stability of impulsive integrodifferential equations with Riemann-Liouville boundary conditions, Adv. Differ. Equ., 2020 (2020), 64. http://dx.doi.org/10.1186/s13662-020-2534-1 doi: 10.1186/s13662-020-2534-1
![]() |
[30] | A. Zada, F. Khan, U. Riaz, T. Li, Hyers-Ulam stability of linear summation equations, Punjab Univ. J. Math., 49 (2017), 19–24. |
[31] |
A. Zada, U. Riaz, F. Khan, Hyers-Ulam stability of impulsive integral equations, Boll. Unione Mat. Ital., 12 (2019), 453–467. http://dx.doi.org/10.1007/s40574-018-0180-2 doi: 10.1007/s40574-018-0180-2
![]() |
[32] |
A. Zada, H. Waheed, J. Alzabut, X. Wang, Existence and stability of impulsive coupled system of fractional integrodifferential equations, Demonstr. Math., 52 (2019), 296–335. http://dx.doi.org/10.1515/dema-2019-0035 doi: 10.1515/dema-2019-0035
![]() |
[33] |
L. Zhang, W. Zahag, X. Liu, M. Jia, Positive solutions of fractional p-Laplacian equations with integral boundary value and two parameters, J. Inequal. Appl., 2020 (2020), 2. http://dx.doi.org/10.1186/s13660-019-2273-6 doi: 10.1186/s13660-019-2273-6
![]() |
1. | Karima M. Oraby, Zeinab S. I. Mansour, Starlike and convexity properties ofq-Bessel-Struve functions, 2022, 55, 2391-4661, 61, 10.1515/dema-2022-0004 | |
2. | Muhey U. Din, Mohsan Raza, Qin Xin, Sibel Yalçin, Sarfraz Nawaz Malik, Close-to-Convexity of q-Bessel–Wright Functions, 2022, 10, 2227-7390, 3322, 10.3390/math10183322 | |
3. | Khaled Mehrez, Mohsan Raza, The Mittag-Leffler-Prabhakar Functions of Le Roy Type and its Geometric Properties, 2024, 2731-8095, 10.1007/s40995-024-01738-1 | |
4. | Abdulaziz Alenazi, Khaled Mehrez, Certain properties of a class of analytic functions involving the Mathieu type power series, 2023, 8, 2473-6988, 30963, 10.3934/math.20231584 | |
5. | İbrahim Aktaş, Luminiţa-Ioana Cotîrlâ, Certain Geometrical Properties and Hardy Space of Generalized k-Bessel Functions, 2024, 16, 2073-8994, 1597, 10.3390/sym16121597 | |
6. | İbrahim Aktaş, Büşra Korkmaz, Some geometric properties of the generalized \(k\)-Bessel functions, 2025, 13, 2321-5666, 15, 10.26637/mjm1301/003 | |
7. | Yücel Özkan, Semra Korkmaz, Erhan Deniz, The Monotony of the q−Bessel functions, 2025, 0022247X, 129439, 10.1016/j.jmaa.2025.129439 | |
8. | Muhammad Abubakr, Mohsan Raza, Khaled Mehrez, Starlikeness and convexity of normalized Miller–Ross function, 2025, 1065-2469, 1, 10.1080/10652469.2025.2509150 | |
9. | Muhammad Abubakr, Mohsan Raza, Abdulaziz Alenazi, Khaled Mehrez, A Study of Certain Geometric Properties and Hardy Spaces of the Normalized Miller-Ross Function, 2025, 13, 2227-7390, 1919, 10.3390/math13121919 |
Tun | Fra | Ita | Ger | |
total data obtained | 273 | 669 | 671 | 670 |
interpolation data | 69 | 168 | 168 | 168 |
Fractal interpolation | Recurrent fractal interpolation | ||
Fi(x,y)=αy+cix+di | α=0.1 | 273.819 | 264.536 |
α=0.2 | 310.896 | 270.743 | |
α=0.3 | 379.131 | 281.771 | |
α=0.5 | 636.935 | 320.554 | |
Fi(x,y)=y1+βy+c′ix+d′i | β=0.015 | 263.342 | 263.252 |
β=0.025 | 263.283 | 263.283 | |
β=0.045 | 263.354 | 263.299 | |
β=0.1 | 263.278 | 263.345 |
Tun | Fra | Ita | Ger | |
total data obtained | 273 | 669 | 671 | 670 |
interpolation data | 69 | 168 | 168 | 168 |
Fractal interpolation | Recurrent fractal interpolation | ||
Fi(x,y)=αy+cix+di | α=0.1 | 273.819 | 264.536 |
α=0.2 | 310.896 | 270.743 | |
α=0.3 | 379.131 | 281.771 | |
α=0.5 | 636.935 | 320.554 | |
Fi(x,y)=y1+βy+c′ix+d′i | β=0.015 | 263.342 | 263.252 |
β=0.025 | 263.283 | 263.283 | |
β=0.045 | 263.354 | 263.299 | |
β=0.1 | 263.278 | 263.345 |