- Open Access
Comparison principle and stability for a class of stochastic fractional differential equations
© Lu et al.; licensee Springer. 2014
- Received: 3 February 2014
- Accepted: 17 July 2014
- Published: 15 August 2014
In this paper, we study a class of stochastic fractional differential equations. We first establish a novel comparison principle for such equations. Then, we use the new comparison principle to obtain some stability criteria, which include the stability in probability, uniform stability in probability, asymptotic stability in probability, and p th moment exponential stability. Finally, an example is provided to illustrate the obtained results.
- comparison principle
- stochastic fractional differential equation
- stability in probability
- uniform stability in probability
- asymptotic stability in probability
- p th moment exponential stability
In recent decades, stochastic models have been applied in many areas such as social science, physical science, finance, control engineering, mechanical, electrical and industry. The stability analysis is one of the most important research topics in stochastic models. There has been a large number of stability results in the literature. For instance, see  and the references therein.
On the other hand, fractional calculus is a mathematical subject with a history of more than 300 years. There have been more and more researchers interested in studying the fractional calculus in the last twenty years. One of the main reasons is that the integer-order calculus and conventional differential equations are no longer suitable tools for many systems and processes, such as viscoelastic system , dielectric polarization , electrode-electrolyte polarization , electrical circuit , electromagnetic waves , heat condition , biological system , quantitative finance , and quantum evolution of complex system . However, such systems can be elegantly described by fractional-order differential equations with the help of the fractional calculus.
In comparison with the classical integer-order calculus, the fractional calculus has natural advantages in describing systems possessing memory and hereditary properties. In recent years, the classical mathematical modeling approaches coupled with the stochastic methods have been used to develop stochastic dynamic models for financial data (stock price). In order to extend this approach to more complex dynamic processes in sciences and engineering operating under internal structural and external environmental perturbations, we establish stochastic fractional differential equations by introducing the concept of dynamics processes operating under a set of linearly independent time-scales.
Recently, the authors in  studied the problem of existence and uniqueness of solutions of the initial value problem of stochastic fractional differential equations. But they did not discuss the stability analysis problem. This situation encourages our present research.
Motivated by the above discussion, in this paper we investigate the stability analysis problem for a class of stochastic fractional differential equations. Different from the traditional Lyapunov stability theory, we first establish a novel comparison principle for stochastic fractional differential equations, and then obtain some stability criteria including the stability in probability, uniform stability in probability, asymptotic stability in probability, p th moment stability of such equations based on the new comparison principle. Finally, we use an example to illustrate our stability results.
The rest of this paper is organized as follows. In Section 2, we introduce the model of a class of stochastic fractional differential equations, some preliminary results and definitions. In Section 3, we construct the comparison principle for stochastic fractional differential equations of Itô-Doob type and obtain some stability criteria including the stability in probability, uniform stability in probability, asymptotic stability in probability, p th moment stability of such equations. An example is provided to illustrate how to apply the developed results in the stability analysis in Section 4. Finally, in Section 5, we conclude the paper with some general remarks.
Throughout this paper, unless otherwise specified, ℝ denotes the set of real numbers, denotes the set of positive real numbers, Z denotes the set of integers and N is the set of positive integers. Let be an m-dimensional Brownian motion defined on a complete probability space , let denote the differential of order α, and let denote the Euclidean norm in .
Definition 2 (R-L fractional derivative )
which is clearly different from the differential with integer order.
Definition 3 (Multi-time scale integral )
depends on the time-scale for each .
Definition 4 (Multi-time scale differential )
where , , , .
Assume that b, , and satisfy the Lipschitz condition and linear growth condition, and thus it follows from  that system (4) has a unique solution . Also, assume that , , , and then system (4) admits a trivial solution or zero solution corresponding to the initial data .
Remark 3 We remark that some classical models are special cases of system (4).
- (i)If in Remark 1, then (4) is reduced to the following Itô-Doob type stochastic differential equation:(5)
- (ii)Letting in (4), then we have the following generalized version of the classical deterministic fractional differential equation:(6)
- (iii)If and , then (4) becomes the following deterministic fractional differential equation:(7)
Definition 5 (Lyapunov stable)
The zero solution of system (4) is said to be Lyapunov stable if for every and , there exists such that for all when .
The zero solution of system (4) is uniformly Lyapunov stable if for every , there exists such that for all when .
The zero solution of system (4) is asymptotically stable if it is Lyapunov stable and there exists such that when .
Definition 6 (Stable in probability)
Definition 7 (Asymptotically stable in probability)
Definition 8 ()
A function is said to belong to the class if , and is strictly increasing in z. A function is said to belong to the class if φ belongs to and φ is convex. A function is said to belong to the class if , , and is concave and strictly increasing in z for each .
In this section, we present our main results. First of all, we give the comparison principle, which plays an important role in the proof of our results.
- (i)() is the largest interval of existence of the maximal solution of the following deterministic fractional differential equation:(8)
- (ii), and for ,(9)
For the solution of (4), exists for .
which contradicts (11). Hence, (10) is satisfied. This completes the proof of Lemma 2. □
As an application of the comparison principle, we will deduce some stability criteria for system (4).
- (1)is a locally Lipschitz continuous in x and uniformly in t compact set of satisfying
- (2)For every , satisfies(12)
If the zero solution of (8) is Lyapunov stable, then the zero solution of (4) is stable in probability. Moreover, if the zero solution of (8) is uniformly stable, then the zero solution of (4) is uniformly stable in probability.
Therefore, from the definition of the stability in probability, we see that the zero solution of (4) is stable in probability. Furthermore, we suppose that the zero solution of (8) is uniformly stable. Noting that the constants , in the above proof are independent of , we can prove similarly that δ does not depend on , which verifies that the zero solution of (4) is uniformly stable in probability. The proof of Theorem 1 is completed. □
Theorem 2 Assume that all the conditions of Theorem 1 are satisfied. If the zero solution of (8) is asymptotically stable, then the zero solution of (4) is asymptotically stable.
when . This together with the definition of asymptotic stability in probability implies that the zero solution of (4) is asymptotically stable in probability. This completes the proof of Theorem 2. □
where , . If the zero solution of (8) is Lyapunov stable, then the zero solution of (4) is pth moment exponentially stable.
Therefore, from the definition of the p th moment exponential stability, we see that the zero solution of (4) is p th moment exponentially stable. The proof of Theorem 3 is completed. □
where , .
For more details about the Mittag-Leffler function, we refer the reader to . It is obvious that the solution of (20) is stable. So, according to Theorem 1, the zero solution of stochastic fractional differential equation (19) is stable in probability.
In this paper, we have established a novel comparison principle for a class of stochastic fractional differential systems. By employing the new comparison principle and Lyapunov stability theory, we obtain some useful stability criteria. These criteria are drawn from the stability of the comparison function with regard to the original system and an inequality constraint condition. As an application, an example is presented to illustrate how to apply the developed results in the stability analysis. The example shows that the proposed method is very convenient.
This work was jointly supported by the National Natural Science Foundation of China (61374080), the Natural Science Foundation of Zhejiang Province (LY12F03010), the Natural Science Foundation of Ningbo (2012A610032), K.C. Wong Magna Fund in Ningbo University, and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
- Mao X: Stochastic Differential Equation and Application. Horwood, Chichester; 1997.Google Scholar
- Bagley RL, Calico RA: Fractional order state equations for the control of viscoelastic structures. J. Guid. Control Dyn. 1991, 14(2):304-311. 10.2514/3.20641View ArticleGoogle Scholar
- Sun HH, Abdelwahad AA, Onaral B: Linear approximation of transfer function with a pole of fractional order. IEEE Trans. Autom. Control 1984, 29(5):441-444. 10.1109/TAC.1984.1103551View ArticleMATHGoogle Scholar
- Ichise M, Nagayanagi Y, Kojima T: An analog simulation of non-integer order transfer functions for analysis of electrode process. J. Electroanal. Chem. Interfacial Electrochem. 1971, 33(2):253-256. 10.1016/S0022-0728(71)80115-8View ArticleGoogle Scholar
- Chen G, Friedman EG: An RLC interconnect model based on Fourier analysis. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 2005, 24(2):170-183.View ArticleGoogle Scholar
- Heaviside O: Electromagnetic Theory. Chelsea, New York; 1971.MATHGoogle Scholar
- Jenson VG, Jeffreys GV: Mathematical Methods in Chemical Engineering. 2nd edition. Academic Press, New York; 1997.MATHGoogle Scholar
- Anastasio TJ: The fractional-order dynamics of brainstem vestibule-oculumotor neurons. Biol. Cybern. 1994, 72(1):69-79. 10.1007/BF00206239View ArticleGoogle Scholar
- Laskin N: Fractional market dynamics. Physica A 2000, 287: 482-492. 10.1016/S0378-4371(00)00387-3MathSciNetView ArticleGoogle Scholar
- Kusnezov D, Bulgac A, Dang GD: Quantum Lévy processes and fractional kinetics. Phys. Rev. Lett. 1999, 82(6):1136-1139. 10.1103/PhysRevLett.82.1136View ArticleGoogle Scholar
- Pedjeu JC, Ladde GS: Modeling, method and analysis. Chaos Solitons Fractals 2012, 45(3):279-293. 10.1016/j.chaos.2011.12.009MathSciNetView ArticleMATHGoogle Scholar
- Kilbas AA, Srivastava HM, Trujillo JJ: Theory and Applications of Fractional Differential Equations. Elsevier, Amsterdam; 2006.MATHGoogle Scholar
- Ladas GE, Lakshmikantham V: Differential Equations in Abstract Spaces. Academic Press, New York; 1972.MATHGoogle Scholar
- Lang S: Real and Functional Analysis. 3rd edition. Springer, New York; 1993.View ArticleMATHGoogle Scholar
- Gihman II, Skorohod AV: Stochastic Differential Equations. Springer, New York; 1972.View ArticleMATHGoogle Scholar
- Jumarie G: Fractional Brownian motions via random walk in the complex plane and via fractional derivative. Comparison and further results on their Fokker-Planck equations. Chaos Solitons Fractals 2004, 22(4):907-925. 10.1016/j.chaos.2004.03.020MathSciNetView ArticleMATHGoogle Scholar
- Jumarie G: New stochastic fractional models for Malthusian growth, the Poissonian birth process and optimal management of populations. Math. Comput. Model. 2006, 44(3-4):231-254. 10.1016/j.mcm.2005.10.003MathSciNetView ArticleMATHGoogle Scholar
- Dong F, Wu Y, Fang Y: Comparison principle and stability of general continuous time Markov jump system. 2. Proceedings of the 2008 International Conference on Computational Intelligence and Security 2008, 186-191.View ArticleGoogle Scholar
- Jumarie G:On the representation of fractional Brownian motion as an integral with respect to . Appl. Math. Lett. 2005, 18(7):739-748. 10.1016/j.aml.2004.05.014MathSciNetView ArticleMATHGoogle Scholar
- Jumarie G: Modified Riemann-Liouville derivative and fractional Taylor series of nondifferentiable functions further results. Comput. Math. Appl. 2006, 51(9-10):1367-1376. 10.1016/j.camwa.2006.02.001MathSciNetView ArticleMATHGoogle Scholar
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