Granular Fuzzy Fractional Financial Systems Governed by Granular Caputo Fractional Derivative
Abstract
:1. Introduction
1.1. Motivation and Contribution
1.2. Fundamental Difference Between Fuzzy and Stochastic Modeling
1.3. Model Formulation
2. Basic Concepts
Granular Laplace Transform for FVFs
3. The Existence and Uniqueness of the Fuzzy Solution to the GFFFS Under -CFD
- (HF − I)
- The functions and are measurable and continuous for each , respectively.
- (HF − II)
- There exists such that for every .
- (HF − III)
- There exists such that
- (HF − VI)
- There exists such that
- The supremum metric
- Step-1:
- The operator is onto itself. Indeed, for every and , we have
- Step-2:
- The operator is continuous. In fact, let be such that . Then,
- For each and employing hypotheses (, we haveBy applying Lebesgue’s dominated theorem, we obtain
- Step-3:
- Next, we need to demonstrate that is a contraction mapping. To do this, let . It is sufficient to demonstrate the existence of a constant satisfying
- Infect, for every , it holds that:By dividing both sides by and taking the supremum over , we obtainIt is important to note that for sufficiently large , we have . This implies that is a contraction. Finally, employing the Banach contraction principle, we have the unique fixed point as the mild solution of the system (22). □
4. Numerical Solution of the GFFFS
4.1. Approximate Solution of FFFS (4) with FICs (5)
Algorithm 1: Numerical algorithm |
Input: The fractional-order , number of partitions N, model parameters, the initial and final time, and the fuzzy initial conditions |
Output: The approximate solution of the GFFF (41) |
Initialization; |
; ; // starting and ending point |
; // step size |
; ; ; // The solution vector |
; // Numerical solution of the system (41) |
for do |
; // plot the numerical solution |
4.2. Error Estimation of the Proposed Numerical Technique
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter Type | Symbol | Values | Description |
---|---|---|---|
System parameters | |||
Savings amount | Uncertain savings range | ||
Cost of investment | Uncertain cost range | ||
Demand elasticity | Uncertain sensitivity | ||
Initial conditions | |||
Interest rate | Uncertain initial interest rate | ||
Demand for investment | Uncertain initial investment demand | ||
Price index | Uncertain initial price variability | ||
Numerical parameters | |||
Fractional order | Memory strength | ||
Time horizon | T | Simulation duration | |
Step size | h | Discretization step | |
-cut level | Fuzzy set’s confidence level |
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Aladsani, F.A.; Muhammad, G.; Elagan, S.K. Granular Fuzzy Fractional Financial Systems Governed by Granular Caputo Fractional Derivative. Mathematics 2025, 13, 1240. https://doi.org/10.3390/math13081240
Aladsani FA, Muhammad G, Elagan SK. Granular Fuzzy Fractional Financial Systems Governed by Granular Caputo Fractional Derivative. Mathematics. 2025; 13(8):1240. https://doi.org/10.3390/math13081240
Chicago/Turabian StyleAladsani, Feryal Abdullah, Ghulam Muhammad, and Sayed K. Elagan. 2025. "Granular Fuzzy Fractional Financial Systems Governed by Granular Caputo Fractional Derivative" Mathematics 13, no. 8: 1240. https://doi.org/10.3390/math13081240
APA StyleAladsani, F. A., Muhammad, G., & Elagan, S. K. (2025). Granular Fuzzy Fractional Financial Systems Governed by Granular Caputo Fractional Derivative. Mathematics, 13(8), 1240. https://doi.org/10.3390/math13081240