Abstract
Under the effect of the Rosenblatt process, the well-posedness and Hyers–Ulam stability of nonlinear fractional stochastic delay systems are considered. First, depending on fixed-point theory, the existence and uniqueness of solutions are proven. Next, utilizing the delayed Mittag–Leffler matrix functions and Grönwall’s inequality, sufficient criteria for Hyers–Ulam stability are established. Ultimately, an example is presented to demonstrate the effectiveness of the obtained findings.
Keywords:
well-posedness; Hyers–Ulam stability; fractional stochastic delay system; Rosenblatt process; delayed Mittag–Leffler matrix function; Krasnoselskii’s fixed point theorem MSC:
34K50; 34K20; 47H10; 34K37
1. Introduction
Fractional stochastic delay differential systems (FSDDSs) and their applications have attracted an abundance of research attention owing to their efficient modeling in many scientific and engineering areas, such as diffusion processes, fluid dynamics, biology, physics, control theory, viscoelastic systems, and many more (see, for instance, [,,,,,,,,,,]). Specifically, many new results on how to represent the solutions to time-delay systems were obtained in the novel study [], and were applied to stability analysis and control problems, for example, representation of solutions [,], controllability analysis [,,,], stability analysis [] and the references therein.
The Wiener–Ito multiple integral of order p is defined as
in terms of the standard Wiener process , where E and are the conditions under which is a normalizing constant. The process , defined by (1), is called the Hermite process. The Hermite process is the fractional Brownian motion (fBm) with Hurst parameter for , while it is not Gaussian for . Additionally, the Hermite process denoted by (1) for is referred to as the Rosenblatt process. Most studies [,,] involve fBm because of its self-similarity, long-range dependence, and more straightforward Gaussian calculus. However, fBm fails in the concrete situation of non-Gaussian, smoothed models. In this situation, the Rosenblatt process is applicable. Non-Gaussian processes like the Rosenblatt process have numerous intriguing characteristics such as stationarity of the increments, long-range dependence, and self-similarity; for more details, see [,,,,,,]. As a result, studying a novel class of FSDDSs driven by the Rosenblatt process seems interesting.
On the other hand, studying the stability of FSDDE solutions is essential, and Hyers–Ulam stability (HUS) is a crucial topic. In 1940, Ulam [] made the first proposal that functional equations are stable during a lecture at Wisconsin University. In 1941, Hyers [] provided a solution to this problem, after which HUS was established. In addition to providing a solid theoretical foundation for the well-posedness and HUS of FSDDEs, the study of the HUS of FSDDEs also provides a solid theoretical foundation for the approximate solution to FSDDEs. When it is rather difficult to acquire the precise solution for the system with HUS, we may substitute an approximate solution for an accurate one, and HUS can, to a certain extent, ensure the dependability of the estimated solution. Recently, many researchers have examined the HUS of diverse kinds of FSDDEs, see [,,,] and the references therein.
However, as far as we know, the standard literature has not dealt with the well-posedness and HUS of nonlinear FSDDEs driven by the Rosenblatt process. Therefore, in this study, we try for the first time to analyze such a topic.
Our study focuses on determining the well-posedness and HUS of nonlinear FSDDEs driven by the Rosenblatt process, taking into account previous research.
where the so-called Caputo fractional derivative of order is denoted by with the lower index zero, represents the state vector, denotes a delay, , , , is any matrix, and is a given function. In the separable Hilbert space , let have value, and let the norm be and the inner product be with parameter . is a Rosenblatt process on another real separable Hilbert space . Furthermore, consider , where .
The remainder of this paper is structured as follows: In Section 2, we present some notations and necessary preliminaries. In Section 3, by utilizing Krasnoselskii’s fixed-point theorem, some sufficient conditions are established for the existence and uniqueness of solutions to system (2). In Section 4, we prove the Hyers–Ulam stability of (2) via Grönwall’s inequality lemma approach. Finally, we give a numerical example to illustrate the effectiveness of the derived results.
2. Preliminaries
During the entire paper, consider to represent the complete probability space with probability measure on and a filtration produced by . For some , consider to represent the Hilbert space of all -measurable th-integrable variables with values in with norm , where the expectation is defined by . Assume that , are two Banach spaces, denotes a non-negative self-adjoint trace class operator on , and is the space of bounded linear operators from to . Let be the space of all Q-Hilbert–Schmidt operators from to , equipped with the norm
Given a norm , let (, ) be the Banach space of all th-integrable and -adapted processes . A norm on can be represented by the matrix norm (column sum):
where . Furthermore, consider
Finally, we assume the initial values
Some of the basic definitions and lemmas employed in this study are discussed.
Definition 1
([]). The delayed Mittag–Leffler-type matrix functions , and are formulated, respectively, by
and
where Γ is a gamma function, denotes the identity matrix, Θ denotes the null matrix, and
Lemma 1
([]). The solution of (2) can be expressed in the following form:
Lemma 2
([]). If satisfies
then
Lemma 3
([]). For such that
after applying Hölder’s inequality and the Kahane–Khinchin inequality, there is a constant such that
Definition 2
Remark 1
([]). A function is a solution of the inequality (6) if and only if there exists a function such that
- (i)
- , .
- (ii)
- , .
Definition 3
([]). The derivative of a function with lower index 0, known as the Caputo fractional of order , is expressed by
Definition 4
([]). The Mittag–Leffler function containing two parameters is defined as
when . Then,
Lemma 4
([]). For any , , and , we obtain
and
Lemma 5
(Grönwall’s inequality []). Let and ℘ be non-negative, continuous functions on , for which the inequality
holds, where is a constant. Then,
Finally, we present Krasnoselskii’s fixed point theorem.
Lemma 6
([]). Assume that is a closed, bounded and non-empty convex subset of a Banach space . If and are mappings from into such that
- (i)
- for every pair ℓ, ,
- (ii)
- is a contraction mapping.
- (iii)
- is continuous and compact.then there is such that .
3. Main Results
In this section, we present and prove the well-posedness and Hyers–Ulam stability results of (2). To prove our main results, the following assumptions are assumed:
- (G1)
- There exist a continuous function and a constant , where , such thatLet and .
- (G2)
- There exist a continuous function and a constant , where , such thatfor all , .
Using Krasnoselskii’s fixed-point theorem, we now prove the existence and uniqueness results.
Theorem 1.
If – hold, then there exists a unique mild solution of the nonlinear stochastic system (2) provided that
where
and
for , , .
Proof.
Applying Lemmas 2 and 3, we obtain
Using Lemma 4 and , we obtain
Additionally, using the Hölder inequality and , we obtain
Substituting (12) into (11), we get
Furthermore, using (12) and , we obtain
From to , (10) becomes
where
As a result, from (7), we obtain for some sufficiently large .
We deal with the set
for each positive number . Let . Applying Lemma 1, we then transform problem (2) into a fixed-point problem and define an operator by
for . Decomposing the operator F, we can define the operators , on as follows:
At this point, we observe that is a convex set, closed and bounded of . Consequently, our proof consists of three essential steps:
Step 1. We show the existence of such that for all ℵ, .
Step 2. We show that is a contraction.
Step 3. We show that is a continuous compact operator.
First, we verify the continuity of . Consider with as in . Thus, using Lebesgue’s dominated convergence theorem and (9), we get, for each ,
This proves the continuity of .
Thereafter, we show that is uniformly bounded on . For each , , we have
This indicates that, on , is uniformly bounded.
Showing that is equicontinuous is still necessary. For each , , and , using (9), we obtain
where
and
Thus,
Now, we can check as , when , 2. For , we get
For , we get
From (5), knowing that is uniformly continuous for we get
Therefore, we have as when , 2, which leads, via (13), to
for all . Then, is compact on via the Arzelà–Ascoli theorem (see []). As a result, has a fixed point ℵ in , in accordance with Lemma 6. Furthermore, ℵ is also a solution of (2) and . Therefore, the system (2) has a mild solution. This completes the proof. □
Next, we verify the Hyers–Ulam stability results via Grönwall’s inequality lemma approach.
Theorem 2.
If the assumptions of Theorem 1 hold, then nonlinear stochastic system (2) has the Ulam–Hyers stability.
Proof.
Assume that ℵ is the unique solution of (2) and is a solution of the inequality (6) with the aid of Theorem 1. Then,
Based on Remark 1, then
can be expressed as
In the same manner as in the proof of Theorem 1 and by consequence of (10), we have
Applying Grönwall’s inequality (Lemma 5), we get
which implies that
where
Therefore, there exists W that satisfies Definition 2. This ends the proof. □
4. An Example
Consider the following nonlinear fractional stochastic delay system driven by the Rosenblatt process:
where , ,
and
Next, by choosing , we get
for all , and , . We set such that in , and then we have . Thus, selecting and , we get
Furthermore, we have
We set such that in , and then we have . Hence,
Finally, we calculate that
which implies that all the assumptions of Theorems 1 and 2 hold. Therefore, system (14) has a unique mild solution ℵ and is Hyers–Ulam stable.
5. Conclusions
In this work, based on fixed-point theory and under the effect of the Rosenblatt process, we proved the existence and uniqueness of solutions of (2). After that, we derived the Hyers–Ulam stability results using the delayed Mittag–Leffler matrix functions and Grönwall’s inequality. Finally, we verified the theoretical results by giving an example with numerical simulations, which showed the effectiveness of the derived results. This is a novel study that proves the well-posedness and Hyers–Ulam stability of (2) using delayed Mittag–Leffler matrix functions.
In future work, studies will focus on the obtained results to ascertain the well-posedness and Hyers–Ulam stability of different types of stochastic delay systems, such as impulsive fractional stochastic delay systems or conformable fractional stochastic delay systems.
Author Contributions
Conceptualization, G.A., M.H., R.U. and A.M.E.; Data curation, G.A., M.H. and A.M.E.; Formal analysis, G.A., R.U., M.H. and A.M.E.; Software, A.M.E.; Supervision, M.H.; Validation, G.A., M.H. and A.M.E.; Visualization, G.A., M.H., R.U. and A.M.E.; Writing—original draft, A.M.E.; Writing—review and editing, G.A., M.H. and A.M.E.; Investigation, M.H. and A.M.E.; Methodology, G.A., M.H. and A.M.E.; Funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript.
Funding
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R45), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are contained within the article.
Acknowledgments
The authors would like to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2024R45), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia for supporting. Also, the authors are very thankful to the editor and the anonymous reviewers for their valuable comments that helped a lot to improve the quality of the paper.
Conflicts of Interest
There are no competing interests.
References
- Obembe, A.D.; Hossain, M.E.; Abu-Khamsin, S.A. Variable-order derivative time fractional diffusion model for heterogeneous porous media. J. Pet. Sci. Eng. 2017, 152, 391–405. [Google Scholar] [CrossRef]
- Coimbra, C.F.M. Mechanics with variable-order differential operators. Ann. Phys. 2003, 12, 692–703. [Google Scholar] [CrossRef]
- Heymans, N.; Podlubny, I. Physical interpretation of initial conditions for fractional differential equations with Riemann–Liouville fractional derivatives. Rheol. Acta 2006, 45, 765–771. [Google Scholar] [CrossRef]
- Rajivganthi, C.; Thiagu, K.; Muthukumar, P.; Balasubramaniam, P. Existence of solutions and approximate controllability of impulsive fractional stochastic differential systems with infinite delay and Poisson jumps. Appl. Math. 2015, 60, 395–419. [Google Scholar] [CrossRef]
- Kilbas, A.A.; Srivastava, H.M.; Trujillo, J.J. Theory and Applications of Fractional Differential Equations; Elsevier Science BV: Amsterdam, The Netherlands, 2006. [Google Scholar]
- Muthukumar, P.; Rajivganthi, C. Approximate controllability of stochastic nonlinear third-order dispersion equation. Int. J. Robust Nonlinear Control 2014, 24, 585–594. [Google Scholar] [CrossRef]
- Ahmed, H.M. Semilinear neutral fractional stochastic integro-differential equations with nonlocal conditions. J. Theoret. Probab. 2015, 28, 667–680. [Google Scholar] [CrossRef]
- El-Borai, M.M.; EI-Nadi, K.E.S.; Fouad, H.A. On some fractional stochastic delay differential equations. Comput. Math. Appl. 2010, 59, 1165–1170. [Google Scholar] [CrossRef]
- Da Prato, G.; Zabczyk, J. Stochastic Equations in Infinite Dimensions; Cambridge University Press: Cambridge, UK, 1992. [Google Scholar]
- Sakthivel, R.; Revathi, P.; Ren, Y. Existence of solutions for nonlinear fractional stochastic differential equations. Nonlinear Anal. 2013, 81, 70–86. [Google Scholar] [CrossRef]
- Jiang, W.; Chen, Y.; Charalambous, T. Consensus of General Linear Multi-Agent Systems with Heterogeneous Input and Communication Delays. IEEE Control Syst. Lett. 2021, 5, 851–856. [Google Scholar] [CrossRef]
- Khusainov, D.Y.; Diblík, J.; Růžičková, M.; Lukáčová, J. Representation of a solution of the Cauchy problem for an oscillating system with pure delay. Nonlinear Oscil. 2008, 11, 276–285. [Google Scholar] [CrossRef]
- Elshenhab, A.M.; Wang, X.T. Representation of solutions for linear fractional systems with pure delay and multiple delays. Math. Meth. Appl. Sci. 2021, 44, 12835–12850. [Google Scholar] [CrossRef]
- Elshenhab, A.M.; Wang, X.T.; Mofarreh, F.; Bazighifan, O. Exact solutions and finite time stability of linear conformable fractional systems with pure delay. Comput. Model. Eng. Sci. 2023, 134, 927–940. [Google Scholar]
- Sathiyaraj, T.; Wang, J.; O’Regan, D. Controllability of stochastic nonlinear oscillating delay systems driven by the Rosenblatt distribution. Proc. Roy. Soc. Edinburgh Sect. A 2021, 151, 217–239. [Google Scholar] [CrossRef]
- Elshenhab, A.M.; Wang, X.T. Controllability and Hyers–Ulam stability of differential systems with pure delay. Mathematics 2022, 10, 1248. [Google Scholar] [CrossRef]
- Varun Bose, C.S.; Udhayakumar, R.; Elshenhab, A.M.; Sathish Kumar, M.; Ro, J.-S. Discussion on the Approximate Controllability of Hilfer Fractional Neutral Integro-Differential Inclusions via Almost Sectorial Operators. Fractal Fract. 2022, 6, 607. [Google Scholar] [CrossRef]
- Liang, C.; Wang, J.; O’Regan, D. Controllability of nonlinear delay oscillating systems. Electron. J. Qual. Theory Differ. Equ. 2017, 2017, 1–18. [Google Scholar] [CrossRef]
- Gao, F.; Oosterlee, C.W.; Zhang, J. A deep learning-based Monte Carlo simulation scheme for stochastic differential equations driven by fractional Brownian motion. Neurocomputing 2024, 574, 127245. [Google Scholar] [CrossRef]
- Feng, J.; Wang, X.; Liu, Q.; Li, Y.; Xu, Y. Deep learning-based parameter estimation of stochastic differential equations driven by fractional Brownian motions with measurement noise. Commun. Nonlinear Sci. Numer. Simul. 2023, 127, 107589. [Google Scholar] [CrossRef]
- El-Borai, M.M.; El-Nadi, K.E.S.; Ahmed, H.M.; El-Owaidy, H.M.; Ghanem, A.S.; Sakthivel, R. Existence and stability for fractional parabolic integro-partial differential equations with fractional Brownian motion and nonlocal condition. Cogent Math. Stat. 2018, 5, 1460030. [Google Scholar] [CrossRef]
- Rosenblatt, M. Independence and dependence. Proc. Berkeley Symp. Math. Statist. Probab. 1961, 2, 431–443. [Google Scholar]
- Shen, G.J.; Ren, Y. Neutral stochastic partial differential equations with delay driven by Rosenblatt process in a Hilbert space. J. Korean Stat. Soc. 2015, 4, 123–133. [Google Scholar] [CrossRef]
- Maejima, M.; Tudor, C.A. Selfsimilar processes with stationary increments in the second Wiener chaos. Probab. Math. Stat. 2012, 32, 167–186. [Google Scholar]
- Shen, G.; Sakthivel, R.; Ren, Y.; Li, M. Controllability and stability of fractional stochastic functional systems driven by Rosenblatt process. Collect. Math. 2020, 71, 63–82. [Google Scholar] [CrossRef]
- Maejima, M.; Tudor, C.A. On the distribution of the Rosenblatt process. Stat. Probab. Lett. 2013, 83, 1490–1495. [Google Scholar] [CrossRef]
- Tudor, C.A. Analysis of the Rosenblatt process. ESAIM Probab. Stat. 2008, 12, 230–257. [Google Scholar] [CrossRef]
- Lakhel, E.H.; McKibben, M. Controllability for time-dependent neutral stochastic functional differential equations with Rosenblatt process and impulses. Int. J. Control Autom. Syst. 2019, 17, 286–297. [Google Scholar] [CrossRef]
- Ulam, S. A Collection of Mathematical Problem; Interscience: New York, NY, USA, 1960. [Google Scholar]
- Hyers, D.H. On the stability of the linear functional equation. Proc. Nati. Acad. Sci. USA 1941, 27, 222–224. [Google Scholar] [CrossRef]
- Kahouli, O.; Albadran, S.; Elleuch, Z.; Bouteraa, Y.; Makhlouf, A.B. Stability results for neutral fractional stochastic differential equations. AIMS Math. 2024, 9, 3253–3263. [Google Scholar] [CrossRef]
- Ahmadova, A.; Mahmudov, N.I. Ulam-Hyers stability of Caputo type fractional stochastic neutral differential equations. Stat. Probab. Lett. 2021, 168, 108949. [Google Scholar] [CrossRef]
- Mchiri, L.; Makhlouf, A.B.; Rguigui, H. Ulam-Hyers stability of pantograph fractional stochastic differential equations. Math. Methods Appl. Sci. 2023, 46, 4134–4144. [Google Scholar] [CrossRef]
- Danfeng, L.; Xue, W.; Tomás, C.; Quanxin, Z. Ulam–Hyers stability of Caputo-type fractional fuzzy stochastic differential equations with delay. Commun. Nonlinear Sci. Numer. Simul. 2023, 121, 107229. [Google Scholar]
- Mattuvarkuzhali, C.; Balasubramaniam, P. pth Moment stability of fractional stochastic differential inclusion via resolvent operators driven by the Rosenblatt process and Poisson jumps with impulses. Stochastics 2020, 92, 1157–1174. [Google Scholar] [CrossRef]
- Wang, J.; Lv, L.; Zhou, Y. Ulam stability and data dependence for fractional differential equations with Caputo derivative. Electron. J. Qual. Theory Differ. Equ. 2011, 2011, 1–10. [Google Scholar] [CrossRef]
- Hale, J.K. Ordinary Differential Equations; Wiley: New York, NY, USA, 1969. [Google Scholar]
- Smart, D.R. Fixed Point Theorems; University Press: Cambridge, UK, 1980. [Google Scholar]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).