1. Introduction
The -Hilfer fractional derivative (-HFrD) is a generalized fractional operator defined with respect to another function , allowing the memory effect to depend on a nonlinear time scale. It unifies several fractional derivatives, including the Riemann–Liouville and Caputo types, and provides a flexible framework for modeling systems with nonlocal and hereditary behavior.
Definition 1. Let and be strictly increasing. For , the left-sided fractional integral of order η with respect to γ is defined by [1] Remark 1. The above operator generalizes the classical Riemann–Liouville fractional integral by replacing the standard time variable with a nonlinear function .
Definition 2. Let be such that , and let be strictly increasing. For , the left-sided Caputo-type fractional derivative of order η with respect to γ is defined by [2] where Remark 2. The operator represents differentiation with respect to the function rather than the standard variable t. Hence, the above derivative can be interpreted as a Caputo-type fractional operator defined on a nonlinear time scale.
Definition 3. Let satisfy and let . Under the same assumptions on γ, the left-sided Hilfer-type fractional derivative of order η and type ϑ with respect to γ is defined by [3] Remark 3. The parameter interpolates between different types of fractional derivatives. Specifically, corresponds to the γ-Riemann–Liouville type derivative, while yields the γ-Caputo type derivative.
Remark 4. For the fundamental case (i.e., ), the above operators simplify significantly. In particular, the γ-Hilfer fractional derivative reduces toThis case is frequently used in applications and highlights the role of the parameter ϑ. Remark 5. If , then all the above operators reduce to the classical Riemann–Liouville, Caputo, and Hilfer fractional derivatives.
Lemma 1. Let , with and . Define [3]Then the following composition formula holds: Remark 6. The above result establishes the inverse relationship between the γ-fractional integral and the γ-Hilfer derivative. It shows that the fractional integral of the Hilfer-type derivative recovers the original function up to correction terms involving generalized initial values.
Fractional stochastic differential equations (FSDEs) that incorporate both Brownian motion and Poisson jump noise offer an advanced framework for modeling systems that exhibit memory effects and stochastic fluctuations. In this context, the Brownian motion component accounts for continuous, minor fluctuations, while the compound Poisson process addresses significant, sudden shocks of varying magnitudes. This integrated approach serves as a powerful mathematical tool for understanding systems affected by the interaction of non-Markovian memory, diffuse randomness, and abrupt changes.
Fractional stochastic systems driven by standard Brownian motion and Poisson jumps have numerous practical applications, particularly in economics. They can effectively model economic growth processes that exhibit long-memory dynamics, wherein past investment decisions and shocks continuously affect present output. Poisson jumps highlight the speed of economic changes in response to policy shifts, financial crises, or market disruptions. These models also enable monitoring of inflation and interest rate fluctuations over time. For instance, they illustrate gradual changes in rates that can accelerate dramatically due to global events or government monetary announcements. In labor economics, these systems reflect variations in job markets over time, influenced by long-term trends as well as sudden layoffs or hiring bursts. Financial institutions, including banks, utilize fractional stochastic models to assess how enduring trends and sudden shocks, such as credit defaults or liquidity crises, will impact their operations. Such models enhance our understanding of dynamic economies that are subject to continuous change and significant, abrupt events.
Existence and uniqueness (Ex-Un) of solutions are fundamental principles in any mathematical model, ensuring that the system’s behavior is distinctly characterized and predictable. Establishing existence confirms the existence of a solution to the system, while uniqueness ensures that this solution is the sole one that aligns with the specified initial conditions. This clarity keeps system dynamics from being unclear, which is important for both theoretical analysis and numerical simulations.
The averaging principle (Av-Pr) for FSDEs provides a structured methodology for comprehending the temporal dynamics of intricate systems, especially concerning memory effects and stochastic perturbations. This principle helps us describe the system more accurately because it averages out terms that change quickly. It keeps important features of both fractional-order memory and stochastic events, like Poisson jumps and Brownian motion. By applying this concept to FSDEs, we can gain insights into the temporal evolution of systems influenced by stochastic processes and fractional dynamics. It also gives you a good starting point for making reduced-order models that keep important statistical and dynamic properties. This method is very useful for studying systems that are hard to study directly or to simulate numerically in the full fractional stochastic context.
As the Ex-Un of solutions is an important issue in the theory of FSDEs. In their investigation of neutral FSDEs with infinite delay, Sakthivel et al. [
4] discovered stability properties and existence results. More generally, Ex-Un results for nonlinear FSDEs driven by Brownian motion have been obtained by combining fixed-point arguments with stochastic analytical methods; see, for instance [
5,
6,
7,
8]. These papers draw attention to the extra mathematical challenges brought about by the interaction of stochastic disturbances and fractional memory.
Numerous stability concepts for FSDEs have been thoroughly examined in addition to well-posedness. Particularly crucial for comprehending how sensitive stochastic systems are to perturbations are mean-square stability and higher-moment stability. Refs. [
9,
10,
11,
12] developed adequate criteria guaranteeing mean-square and higher-moment stability for a number of fractional stochastic model classes in this way.
In recent years, the Av-Pr and controllability have also attracted significant attention. Several notable results concerning averaging for fractional stochastic systems were obtained in [
13,
14,
15]. Likewise, controllability properties of FSDEs, especially for systems involving delays, neutral terms, and nonlocal conditions, have been investigated extensively; see [
16,
17].
The following is a summary of the main contributions of this study:
To the best of my knowledge, there is no research on -Hilfer fractional stochastic systems with standard Brownian motion and Poisson jumps that addresses existence, uniqueness, and Av-Pr in the pth moment.
Most existing results on existence, uniqueness, and the Av-Pr for fractional stochastic systems have been established under standard Brownian motion. In this work, we extend these results to systems driven by both Brownian motion and Poisson jumps.
Most existing results for FSDEs are derived in the mean-square sense. In contrast, this work presents results in the more general pth moment, providing broader applicability.
All results are based on the -HFrD, a generalized fractional operator that encompasses several fractional operators.
We study the following FSDEs with delay:
here,
denotes the state vector, modeled as a stochastic process.
denotes the
-HFrD of order
with parameter
.
is the delay time, and
is continuous.
The following mappings are measurable and continuous:
Furthermore,
denotes an
-dimensional Brownian motion defined over the probability space
. Consider
as a
-finite measurable space. Denote by
the counting measure of a stationary Poisson point process
on
. The compensated Poisson random measure
is defined as
The paper is structured as follows:
Section 2 presents essential preliminaries on fractional calculus, stochastic analysis, and functional spaces.
Section 3 addresses the Ex-Un of the proposed system. The Av-Pr is investigated in
Section 4. Numerical approximations and simulation results based on a Euler–Maruyama approach are provided in
Section 5. Finally, concluding remarks and potential directions for future research are discussed in
Section 6 and
Section 7, respectively.
3. Existence and Uniqueness
By applying the Banach contraction principle (BCP), we first establish the Ex-Un results for solutions of FSDEs with respect to the
-HFrD in the
p-th moment. The space
is defined as the set of all measurable and
-adapted processes
such that
It is straightforward to verify that
is a Banach space. The next step is to define an operator
such that
, and
Lemma 5. Given that and hold, the operator ℑ is well defined.
Proof. Suppose
for all
. Then, by applying Equation (
5) and Lemma 2, we have
□
Lemma 3 yields the following result:
where
.
According to
, we acquire
Consequently, we have:
By Equations (
7) and (
9), we get
By invoking
, Equation (
10) yields the following:
Applying Burkholder–Davis–Gundy Inequality (BDG-I) and Lemma 3, we obtain
where
and
.
Under assumptions
and
, we have the following:
Thus,
, we get the following:
From Equations (
14) and (
12),
From Lemma 2, assumptions
and
, and Lemma 4, it follows that:
here
.
By employing Equations (
11), (
15) and (
16) in (
6), we obtain
. Therefore, the mapping
is well defined.
Next, we present an important lemma that plays a crucial role in proving the theorem on the Ex-Un of the solution to Equation (
3).
Lemma 6. For any , and , we havewhereis the Gamma function. Proof. Let
be arbitrary. Consider the following fractional differential equation involving the
-HFrD of the form:
□
By applying Equation (
1) to both sides of Equation (
18) and using the following Lemma 1
we obtain
From [
19], it follows that the Mittag-Leffler function (MLF)
is a solution of Equation (
18). Hence,
Therefore, the following equality holds:
Hence
In the next theorem, the Ex-Un of solutions are demonstrated by showing that the operator
ℑ is contractive under an appropriately weighted norm. In this context, the MLF
serves as the weight function.
Theorem 1. Assume that and hold, and . If there exists a constant such thatwhere Ψ
is defined in (
24),
then Equation (
3)
admits a unique solution on . Proof. First, choose a fixed
such that
where
□
We now define a weighted norm
on the space
as
The norms and are equivalent. Therefore, the space is complete.
To prove that
ℑ is a contraction mapping, let
. From Equation (
4) and the bounds established in Lemma 2, we obtain:
Using the Lemma 3 and
, we obtain
However, by applying
and the BHD-I, we deduce:
By applying Lemma 2, assumptions
and
, together with Lemma 4, we obtain the following:
For
, one has
For
:
Perform change of variables:
Thus, from Equation (
29), we obtain
Therefore, from Equation (
27), it follows that
Therefore, Equation (
28) implies that
From Equation (
26), it follows that
Here,
is given in Equation (
24). This result indicates that, by using the definition of
from Equation (
25),
Application of Lemma 6 yields the following result:
From (
38), we obtain
where
Since
, it follows that
, and hence
Therefore, the operator ℑ is a contraction on the .
By the BCP,
ℑ admits a unique fixed point in
, which is the unique solution of Equation (
3).
5. Application
In this section, we introduce a population model that is governed by a -Hilfer fractional stochastic delay differential equation.
We consider the following system
with the initial history condition
where
is a given continuous function.
The assumptions of the Theorem 1 are clearly satisfied, which indicates that the system Equation (
66) has a solution. We applied the Euler–Maruyama method to solve the system Equation (
66), and the results are shown.
Figure 1 and
Figure 2 present numerical simulations of the
-Hilfer fractional stochastic population model with delay and Poisson jumps.
Figure 1 illustrates a representative trajectory of the population process, while
Figure 2 displays the corresponding jump increments generated by the compound Poisson process.
Figure 3 and
Figure 4 present numerical simulations of the
-Hilfer fractional stochastic population model with delay and Poisson jumps.
Figure 3 shows five independent sample paths together with their mean trajectory, illustrating the combined effects of memory, delay, diffusion, and jump disturbances.
Figure 4 depicts the corresponding mean evolution of the population process.