Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 3830529, 13 pages
http://dx.doi.org/10.1155/2016/3830529

Research Article

Bipartite Fuzzy Stochastic Differential Equations with Global Lipschitz Condition

Marek T. Malinowski

Institute of Mathematics, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland

Received 27 April 2016; Revised 25 September 2016; Accepted 13 October 2016

Academic Editor: Leonid Shaikhet

Copyright © 2016 Marek T. Malinowski. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We introduce and analyze a new type of fuzzy stochastic differential equations. We consider equations with drift and diffusion terms occurring at both sides of equations. Therefore we call them the bipartite fuzzy stochastic differential equations. Under the Lipschitz and boundedness conditions imposed on drifts and diffusions coefficients we prove existence of a unique solution. Then, insensitivity of the solution under small changes of data of equation is examined. Finally, we mention that all results can be repeated for solutions to bipartite set-valued stochastic differential equations.

1. Introduction

Stochastic differential equations are often used in modelling dynamics of uncertain physical systems, where it is assumed that randomness and stochastic noises have an influence on a considered system. The theory of such equations involving stochastic integrals is well established (see, e.g., [1–3]). On the other hand in modelling of many real-world processes there appears uncertainty of different kind than randomness, namely, trying to describe a physical system one encounters, for instance, an imprecision of measurement equipment, imperfect human judgments, and opinions on parameters of such system. These are also symptoms of uncertainty but they do not locate in randomness or stochastic noises. This uncertainty is well treated by fuzzy set theory (c.f. [4–6]). Owing to this theory it is possible to handle mathematically such linguistic opinions, for example, “low pressure,” “high temperature,” and “about 7%.” A usage of fuzzy sets gives ability to study deterministic fuzzy differential equations in modelling various phenomena which include imprecision [7–10]. Moreover, some successful attempts of combining two kinds of uncertainties, that is, randomness and fuzziness, were undertaken for petroleum contamination [11], optimal tracking design of stochastic fuzzy systems [12], random fuzzy differential equations [13–16], stochastic fuzzy neural networks [17, 18], civil engineering and mechanics [19], Markov chains with fuzzy states [20], fuzzy martingales [21], Petri nets [22], optimization [23], ballast water management [24], filtering of fuzzy stochastic systems [25], and fuzzy stochastic differential equations [26–30].

The latter topic on fuzzy stochastic differential equations is quite new and still developed. In papers [26–28] we considered such equations in their natural integral form generalizing one of the crisp stochastic differential equations, that is,

where is a random fuzzy set-valued drift coefficient, is a random single-valued diffusion coefficient, and is a fuzzy random variable. We investigated the problem of existence of a unique solution, since it is almost impossible to find explicit forms of solutions to such equations. This is very similar to the theory of crisp stochastic differential equations. However, unlike crisp equations, fuzzy set-valued equations exhibit new qualitative properties of their solutions. Namely, we mean here nondecreasing (in time) diameter of solution’s values, which determines that uncertainty located in fuzziness cannot decrease as time increases. This could be an obstacle in some concrete situations, when an expert knows that fuzziness should be decreasing in his system. Therefore, in the works [29, 30], we proposed to study fuzzy stochastic differential equations in integral form

If one would consider this equation in the crisp setting, then it would be no difference from previous equation. However, these two equations are not equivalent in fuzzy environment. Solutions of the second equation have nonincreasing fuzziness of their values. This property does not refer to solutions of crisp equations. Although some potential applications of fuzzy stochastic differential equations in finance, biology, control systems, and physics were studied, for example, in [26, 28, 30], there is still a need of a further development in this area to know better nature of these equations and properties of their solutions.

In this paper we propose to join two equations mentioned above in a one equation

This way we introduce a new kind of fuzzy stochastic differential equations which are more general than those studied in our earlier works and mentioned above. Due to the new form of equations with integrals at both sides they will be called the bipartite fuzzy stochastic differential equations. Solutions to such equations may lose property of monotonicity of fuzziness. However, this can be an advantage, since it can allow for future examinations of periodic solutions. In current paper we initiate investigations of the bipartite fuzzy stochastic differential equations. Under the Lipschitz and boundedness conditions imposed on the drift and diffusion coefficients, existence of a unique solution is proved. It is also shown that the solution is stable with respect to small changes of equation’s data; that is, the solution does not change much when the changes of drift and diffusion coefficients and initial value are small. This shows that the theory introduced in the paper is well-posed. We also indicate that parallel to bipartite fuzzy stochastic differential equations one can consider bipartite set-valued stochastic differential equations and all the results established for the first equations can be easily repeated for the second equations.

The subsequent part of the paper is organized as follows: in Section 2 we collect a prerequisite knowledge on set-valued random variables, set-valued stochastic processes, fuzzy sets, fuzzy random variables, and fuzzy stochastic Lebesgue-Aumann integral. This is done for convenience of the reader. Section 3 is a main part of the paper. The bipartite fuzzy stochastic differential equations are introduced here. We prove existence and uniqueness of solution to such equations and study properties of solutions.

2. Preliminaries

For a convenience of the reader we set up a framework which we work with.

Let be the set of all nonempty, compact, and convex subsets of . This set can be supplied with the Hausdorff metric which is defined by

where denotes a norm in . Then the metric space is complete and separable (see [31]). Also, the addition and scalar multiplication in are defined as follows: for , ,

Let be a complete probability space and denote the family of -measurable set-valued mappings (set-valued random variable) such that

A set-valued random variable is called -integrally bounded, , if there exists such that for any and with . It is known (see [32]) that is -integrally bounded iff is in , where is a space of equivalence classes (with respect to the equality -a.e.) of -measurable random variables such that . Let us denote

The set-valued random variables are considered to be identical, if holds -a.e.

Let , and denote . Let the system be a complete, filtered probability space with a filtration satisfying the usual hypotheses; that is, is an increasing and right continuous family of sub--algebras of , and contains all -null sets. We call a set-valued stochastic process, if for every a mapping is a set-valued random variable. We say that a set-valued stochastic process is -continuous, if almost all (with respect to the probability measure ) its paths, that is, the mappings , are the -continuous functions. A set-valued stochastic process is said to be -adapted, if for every the set-valued random variable is -measurable. It is called measurable, if is a -measurable set-valued random variable, where denotes the Borel -algebra of subsets of . If is -adapted and measurable, then it will be called nonanticipating. Equivalently, is nonanticipating iff is measurable with respect to the -algebra which is defined as follows:

where . A set-valued nonanticipating stochastic process is called -integrally bounded, if there exists a measurable stochastic process such that and for a.a. . By we denote the set of all equivalence classes (with respect to the equality -a.e., denotes the Lebesgue measure) of nonanticipating and -integrally bounded set-valued stochastic processes.

A fuzzy set in (see [4]) is characterized by its membership function (denoted by again) and (for each ) is interpreted as the degree of membership of in the fuzzy set . As the value expresses “degree of membership of in” or a “degree of satisfying by a property,” one can work with imprecise information. Obviously, every ordinary set in is a fuzzy set, since then if and if .

Let denote the fuzzy sets such that for every , where for and . Note that the set can be embedded into by the embedding defined as follows: for we have if , and if .

Addition and scalar multiplication in fuzzy set space can be defined levelwise (see [33]):

where , , and .

Let . If there exists such that then we call the Hukuhara difference of and and we denote it by . Note that . Also may not exist, but if it exists it is unique. For and we have the following:

(P1),(P2)the Hukuhara difference exists iff exists. Moreover, .

Define by the expression

The mapping is a metric in . It is known that is a complete metric space, but it is not separable and it is not locally compact. For every , , one has (see, e.g., [14, 34])

(P3),(P4),(P5),(P6),(P7),(P8).

A mapping is said to be a fuzzy random variable (see [34]), if is an -measurable set-valued random variable for all . It is known from [35] that is the fuzzy random variable iff is -measurable, where denotes the Skorohod metric in and denotes the -algebra generated by the topology induced by . A fuzzy random variable is said to be -integrally bounded, , if belongs to . By we denote the set of all -integrally bounded fuzzy random variables, where we consider as identical if holds -a.e.

We call a fuzzy stochastic process, if for every the mapping is a fuzzy random variable. We say that a fuzzy stochastic process is -continuous, if almost all (with respect to the probability measure ) its trajectories, that is, the mappings , are the -continuous functions. A fuzzy stochastic process is called -adapted, if for every the multifunction is -measurable for all . It is called measurable, if is a -measurable multifunction for all , where denotes the Borel -algebra of subsets of . If is -adapted and measurable, then it is called nonanticipating. Equivalently, is nonanticipating iff for every the set-valued random variable is measurable with respect to the -algebra . A fuzzy stochastic process is called -integrally bounded (), if there exists a measurable stochastic process such that and for a.a. . By we denote the set of nonanticipating and -integrally bounded fuzzy stochastic processes.

In the whole paper, notation stands for abbreviation of , where are some random elements. Also we will write instead of , where , are some stochastic processes. Similar notations will be used for inequalities.

Let , . For such process we can define (see, e.g., [26]) the fuzzy stochastic Lebesgue-Aumann integral which is a fuzzy random variable

Then (from now on we do not write the argument ) is understood as . For the fuzzy stochastic Lebesgue-Aumann integral we have the following properties (see [26]).

Proposition 1. Let . If then

(i) belongs to ,(ii)the fuzzy stochastic process is -continuous,(iii),(iv)for every it holds .

3. Main Results

Let denote an -dimensional -Brownian motion defined on , . The process is defined as follows: , where are the independent, one-dimensional -Brownian motions, and the symbol denotes transposition. Similarly stays for an -dimensional Brownian motion which is assumed to be independent of .

In the paper we make an examination of initial value problem for fuzzy stochastic differential equations of a new form

with , , , and being a fuzzy random variable. Note that problem (12) can be reformulated as

since we consider -dimensional Brownian motion , -dimensional Brownian motion , , , where for each and .

The way of writing fuzzy stochastic differential equations in differential forms (12) and (13) is symbolic only, because these equations are always considered as integral equations:

where the first integrals on both sides are the fuzzy stochastic Lebesgue-Aumann integrals and the remaining integrals are the crisp stochastic Itô integrals.

One can observe that if and for , then (14) takes form

Such equations were studied in [26–28]. On the other hand if and for , then (14) reduces to

and only investigations concerning these equations are presented in [29, 30]. If the data , , are the single-valued and singleton-defined mappings in (15) and (16), then we arrived at the same type of crisp stochastic differential equation. However, in fuzzy case, (15) and (16) are of different type, because fuzzy solutions to fuzzy equations (15) and (16) exhibit different geometric properties. The solutions to (15) have nondecreasing fuzziness in their values as increases; that is, with for every the mappings are nondecreasing (Theorem  3.8 [26]), but for the solutions to (16) the mappings are nonincreasing (Theorem  3.3 [30]).

In this paper we establish a new kind of fuzzy stochastic differential equations (14) by joining (15) and (16). Therefore (14) is called the bipartite fuzzy stochastic differential equation. The solutions to (14) can lose property that the mappings are monotone. Indeed, the fuzzy stochastic Lebesgue-Aumann integral on the left-hand side of (14) is an item which affects monotonicity of functions . It makes them nonincreasing ones, but simultaneously the fuzzy stochastic Lebesgue-Aumann integral on the right-hand side of (14) forces that the functions do not decrease. However, the loss of monotonicity could be an advantage in the future, since it could open a gate for future studies of periodic solutions to fuzzy stochastic differential equations.

Note that, using (P1) and (P2), (14) can be viewed as

Hence, without loss of generality, we can consider bipartite fuzzy stochastic differential equations of the following integral form:

where , , and are the independent one-dimensional -Brownian motions, and is a fuzzy random variable.

Below we write what we mean by a solution to bipartite fuzzy stochastic differential equation. Let , .

Definition 2. Let a fuzzy stochastic process satisfy the following: (i) , (ii) is -continuous, and (iii) it holds (18). If , then is said to be the local solution to bipartite fuzzy stochastic differential equation (18), and if , then is called the global solution to (18). A local solution to (18) is said to be unique, if , where is any other local solution to (18). The uniqueness of the global solution to (18) is defined similarly.

Since existence of Hukuhara differences in (18) depends on , existence of solution to (18) cannot be independent of . This fact differs bipartite fuzzy differential equations from crisp stochastic differential equations.

In what follows we begin our study with a first and most important issue of existence and uniqueness of solutions to (18). In the paper we require that , , () satisfy the following:

(A0),(A1)the mappings are -measurable and are -measurable,(A2)there exists a constant such that for -a.a. and for every it holds

(A3)there exists such that for -a.a. it holds:

(A4)there exists a constant such that the sequence of the fuzzy mappings described as

 and for

 is well defined; that is, in particular, the Hukuhara differences appearing above do exist.

Remark 3. Assume that (A0)–(A4) are satisfied for , , , . Then the mappings described in (A4) are the fuzzy stochastic processes, and they are -continuous and belong to .

A preliminary result is on the sequence and it shows that is uniformly bounded. As we intend to use as the sequence of approximate solutions, we will be able to infer later on the fact that the exact solution is bounded as well.

Proposition 4. Assume that (A0)–(A4) are satisfied for , , , . Then, for the sequence defined like in (22) we have

where and .

Proof. Denote for and . Applying properties (P4), (P6), and (P5) we arrive at

Further,

Invoking Proposition 1 and the Doob inequality we get

By assumptions (A2) and (A3) we obtain

Due to the last inequality we can infer that for . Hence for

Applying Gronwall’s inequality we arrive at for . This allows us to infer that .

In what follows we formulate an existence and uniqueness theorem for solutions to the bipartite fuzzy stochastic differential equations.

Theorem 5. Assume that conditions (A0)–(A4) are satisfied. Then the bipartite fuzzy stochastic differential equation (18) has a unique local solution.

Proof. Denote for and . Then for applying properties (P4), (P8), and (P5) we can write

By Proposition 1, Doob’s inequality, and assumptions (A2) and (A3) we obtain

where . Moreover for we obtain . Thus one can infer that for

Using Chebyshev’s inequality and (31) we arrive at

Since the series is convergent, due to the Borel-Cantelli lemma we obtain

Now, similarly like in [26], we infer that there exists a -continuous fuzzy stochastic process such that , as . It can also be verified that , .
We shall show that is a solution to (18). Indeed, let us notice that

By Proposition 1 and Itô’s isometry, assumption (A2), and Lebesgue’s Dominated Convergence Theorem we get

Hence for every

Thus we can infer that for every

Now, since the processes are -continuous, we get

This shows that is a solution (possibly a local solution) to (18).
What is left is to prove that the solution is unique. Let us assume that are two solutions to the bipartite fuzzy stochastic differential equation (18). Denote for . Let us notice that for every we have

Invoking Gronwall’s inequality we get for , which leads to the conclusion that . This ends the proof.

As we mentioned earlier the sequence can be treated as a sequence of approximate solutions. The next result presents an upper bound for the error of th approximation .

Proposition 6. Assume that (A0)–(A4) hold for , , , and ’s. Then, for the approximations defined in (22) and the exact solution to the bipartite fuzzy stochastic differential equation (18) we have

where is like in (31).

Proof. Denote for . Notice that

Applying (31) we arrive at

Now invoking Gronwall’s inequality we can write

This leads us to the inequality .

As an immediate consequence of the assertion presented above, we have the following property as , which, together with Proposition 4, allows us to find a bound for the expression . Another estimation is contained in the next claim.

Proposition 7. Assume that conditions (A0)–(A4) are satisfied for , , , and ’s. Let denote unique (possibly local) solution to (18). Then it holds

Proof. Denote for . Observe that

Further it can be verified that

Hence, by Gronwall’s inequality we can infer that

The next part of this section is focused on well-posedness of the theory of bipartite fuzzy stochastic differential equations. We shall prove that the solutions are insensitive with respect to small changes of the equation’s data. We start with insensitivity with respect to initial value .

Let denote solutions to the bipartite fuzzy stochastic differential equation (18) and

respectively. The initial values can differ on a set of positive probability and the remaining data are the same.

Theorem 8. Assume that satisfy condition (A0) and , , , , satisfy (A1)–(A3). Suppose that , , , ’s satisfy (A4) and , , , ’s fulfill (A4) and this happens for a common . Then, for the unique local (or global) solutions to the bipartite fuzzy stochastic differential equations (18) and (48) we have

Proof. The unique solutions to (18) and (48) exist owing to Theorem 5. For one gets

Invoking Gronwall’s inequality we infer that

for every .

By this theorem one can infer that the solutions to (18) and (48) are close to each other provided that is small. As an immediate consequence of that, one can state that solution to (18) depends continuously on . Indeed, consider (18) and

Let , denote solutions to (18) and (52), respectively.

Corollary 9. Assume that , satisfy (A0), and , , , , satisfy (A1)–(A3). Suppose that , , , ’s satisfy (A4) and , , , ’s fulfill (A4) for each and this happens for a common . Suppose that

Then for the unique solution to (18) and the unique solutions to the bipartite fuzzy stochastic differential equations we have

Now, we consider (18) and

to investigate continuous dependence of solution to (18) with respect to coefficients , , and ’s. Let , denote solutions to (18) and (55), respectively.

Theorem 10. Assume that satisfy (A0) and , , , , satisfy (A1)–(A3) with the same constant and function . Assume that condition (A4) is satisfied with the same constant   for , , , ’s and for , , , ’s . Suppose that for every it holds

Then, for the unique solution to (18) and the unique solutions to (55) we have

Proof. Owing to Theorem 5 the solutions to (18) and to (55) exist on and are unique. For we have

where

Invoking Gronwall’s inequality we obtain

The thesis follows by assumptions (56).

4. Application to Bipartite Set-Valued Stochastic Differential Equations

In this part of the paper we present some results concerning bipartite set-valued stochastic differential equations. We do this because set-valued analysis constitutes a branch of research also in context of set-valued differential equations [36]. We discuss only main issues without including proofs. This is because the results presented here are parallel to those established in Section 3 for bipartite fuzzy stochastic differential equations. All the inference methods are similar to those contained in preceding section.

By the bipartite set-valued stochastic differential equations written in their integral form we mean the following equations:

where is a set-valued random variable, , , and are the independent one-dimensional -Brownian motions. The first and the second integral in (61) are the set-valued stochastic Lebesgue integral, while the next integrals are the -valued stochastic Itô integrals.

Denote , .

Definition 11. By a local solution (in the case ) to (61) we mean a set-valued stochastic process satisfying the following: (i) , (ii) is -continuous, and (iii) it holds

If , then is said to be a global solution to (61). A (local or global) solution to (61) is unique iff , where is any other solution to (61).

We shall state an existence and uniqueness theorem for solutions to (61) under the following conditions:

(S0),(S1)the mappings are -measurable and are -measurable,(S2)there exists a constant such that for -a.a. and for every it holds

(S3)there exists such that for -a.a. it holds

(S4)there exists a constant such that the sequence of the set-valued mappings described as

 and for

 is well defined.

Using the sequence defined in (S4) and proceeding similarly like in the proof of Theorem 5 we are able to derive the following result.

Corollary 12. Assume that , , , and ’s satisfy conditions (S0)–(S4). Then the bipartite set-valued stochastic differential equation (61) has a unique (possibly local) solution.

The theory of bipartite set-valued stochastic differential equations is well-posed. Below, by stating two corollaries, we indicate that the set-valued solution to (61) possesses properties of continuous dependence on initial set-valued random variable and coefficients , , ’s.

Corollary 13. Assume that , satisfy (S0) and , , satisfy conditions (S1)–(S3). Assume that , , , ’s satisfy (S4) and , , , ’s satisfy (S4) as well and this happens for a common . Suppose that

Then for the unique solution to (61) and the unique solutions to the bipartite set-valued stochastic differential equations

it holds

Denote by the solutions to bipartite set-valued stochastic differential equations (61) and

respectively.

Corollary 14. Assume that satisfies (S0) and , , , , , satisfy (S1)–(S3) with the same constant and the same function . Assume that condition (S4) is satisfied with the same constant   for , , , ’s and for , , , ’s . Suppose that for every it holds

Then, for the unique solution to (61) and the unique solutions to (70) we have

Let us only mention that besides properties mentioned above the other properties like boundedness of th approximation and boundedness of set-valued solution can also be stated.

5. Concluding Remarks

The paper introduces very first study on so-called bipartite fuzzy stochastic differential equations. Solutions of equations considered previously (cf. [26–30]) had a property that their trajectory values (the values are fuzzy sets) had either nondecreasing or nonincreasing diameter in time. Now, owing to new equations examined in this paper, we open a way to consider fuzzy stochastic differential equations with solutions that have trajectories of nonmonotone diameter of their values. Since seeking explicit solutions to such the equations is mostly without success, we provide a study on existence of a unique solution. This is achieved under conditions of Lipschitz coefficients of drift and diffusion. Then we indicate that solution is bounded and insensitive under small changes of coefficients and initial value. This confirms that the theory of the new equations investigated in this paper is well-posed. Finally, we show that all results achieved can be easily applied to bipartite set-valued stochastic differential equations.

The current study can be a starting point for some future investigations. For instance, from now on it is possible to speak on periodic diameter of solutions for fuzzy stochastic differential equations. Hence, a study in this direction would be interesting. Moreover, one can try to use some weaker assumptions (than Lipschitz conditions) imposed on coefficients to get existence of a unique solution; different kinds of stabilities of solutions are also of interest.

Competing Interests

The author declares that he has no competing interests.

References

L. Arnold, Stochastic Differential Equations: Theory and Applications, John Wiley & Sons, New York, NY, USA, 1974.

I. I. Gihman and A. V. Skorohod, Stochastic Differential Equations, Springer, Berlin, Germany, 1972.

B. Øksendal, Stochastic Differential Equations: An Introduction with Applications, Springer, Berlin, Germany, 2003.

L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, pp. 338–353, 1965.

L. A. Zadeh, “From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions,” International Journal of Applied Mathematics and Computer Science, vol. 12, no. 3, pp. 307–324, 2002.

H.-J. Zimmermann, Fuzzy Set Theory and Its Applications, Kluwer Academic, Boston, Mass, USA, 2nd edition, 1992.

V. Lakshmikantham and R. N. Mohapatra, Theory of Fuzzy Differential Equations and Inclusions, vol. 6, Taylor & Francis, Ltd, London, UK, 2003.

L. T. Gomes, L. C. de Barros, and B. Bede, Fuzzy Differential Equations in Various Approaches, Springer, Berlin, Germany, 2015.

T. Allahviranloo and M. Chehlabi, “Solving fuzzy differential equations based on the length function properties,” Soft Computing, vol. 19, no. 2, pp. 307–320, 2014.

F. Rabiei, F. Ismail, A. Ahmadian, and S. Salahshour, “Numerical solution of second-order fuzzy differential equation using improved Runge-Kutta Nystrom method,” Mathematical Problems in Engineering, vol. 2013, Article ID 803462, 10 pages, 2013.

J. B. Li, G. H. Huang, A. Chakma, G. M. Zeng, and L. Liu, “Integrated fuzzy-stochastic modeling of petroleum contamination in subsurface,” Energy Sources, vol. 25, no. 6, pp. 547–563, 2003.

B.-S. Chen, B.-K. Lee, and L.-B. Guo, “Optimal tracking design for stochastic fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 6, pp. 796–813, 2003.

Y. Feng, “Fuzzy stochastic differential systems,” Fuzzy Sets and Systems, vol. 115, no. 3, pp. 351–363, 2000.

M. T. Malinowski, “Random fuzzy differential equations under generalized Lipschitz condition,” Nonlinear Analysis. Real World Applications. An International Multidisciplinary Journal, vol. 13, no. 2, pp. 860–881, 2012.

M. T. Malinowski, “Random fuzzy fractional integral equations—theoretical foundations,” Fuzzy Sets and Systems, vol. 265, pp. 39–62, 2015.

J. Y. Park and J. U. Jeong, “On random fuzzy functional differential equations,” Fuzzy Sets and Systems, vol. 223, pp. 89–99, 2013.

L. Chen, R. Wu, and D. Pan, “Mean square exponential stability of impulsive stochastic fuzzy cellular neural networks with distributed delays,” Expert Systems with Applications, vol. 38, no. 5, pp. 6294–6299, 2011.

P. Xiong and L. Huang, “On pth moment exponential stability of stochastic fuzzy cellular neural networks with time-varying delays and impulses,” Advances in Difference Equations, vol. 2013, article 172, 2013.

B. Möller and M. Beer, Fuzzy Randomness. Uncertainty in Civil Engineering and Computational Mechanics, Springer, Berlin, Germany, 2004.

R. Kleyle and A. de Korvin, “Transition probabilities for Markov chains having fuzzy states,” Stochastic Analysis and Applications, vol. 15, no. 4, pp. 527–546, 1997.

M. Stojaković, “Fuzzy martingales—a simple form of fuzzy processes,” Stochastic Analysis and Applications, vol. 14, no. 3, pp. 355–367, 1996.

F. Liu, M. Heiner, M. Yang, and J. Bourdon, “Fuzzy stochastic Petri nets for modeling biological systems with uncertain kinetic parameters,” PLOS ONE, vol. 11, no. 2, Article ID e0149674, 2016.

S. Wang and J. Watada, Fuzzy Stochastic Optimization, Springer, New York, NY, USA, 2012.

L. Jing, B. Chen, B. Zhang, and H. Peng, “A hybrid fuzzy stochastic analytical hierarchy process (FSAHP) approach for evaluating ballast water treatment technologies,” Environmental Systems Research, vol. 2, article 10, 2013.

Z. Li and X. Yang, “Delay-dependent robust filtering for a class of fuzzy stochastic systems,” Abstract and Applied Analysis, vol. 2014, Article ID 673956, 10 pages, 2014.

M. T. Malinowski, “Some properties of strong solutions to stochastic fuzzy differential equations,” Information Sciences, vol. 252, pp. 62–80, 2013.

M. T. Malinowski, “Fuzzy and set-valued stochastic differential equations with local lipschitz condition,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 5, pp. 1891–1898, 2015.

M. T. Malinowski and R. P. Agarwal, “On solutions to set-valued and fuzzy stochastic differential equations,” Journal of the Franklin Institute, vol. 352, no. 8, pp. 3014–3043, 2015.

M. T. Malinowski, “Fuzzy stochastic differential equations of decreasing fuzziness: approximate solutions,” Journal of Intelligent & Fuzzy Systems, vol. 29, no. 3, pp. 1087–1107, 2015.

M. T. Malinowski, “Stochastic fuzzy differential equations of a nonincreasing type,” Communications in Nonlinear Science and Numerical Simulation, vol. 33, pp. 99–117, 2016.

S. Hu and N. S. Papageorgiou, Handbook of Multivalued Analysis, Vol. I: Theory, Kluwer Academic, Boston, Mass, USA, 1997.

F. Hiai and H. Umegaki, “Integrals, conditional expectations, and martingales of multivalued functions,” Journal of Multivariate Analysis, vol. 7, no. 1, pp. 149–182, 1977.

P. Diamond and P. Kloeden, Metric Spaces of Fuzzy Sets: Theory and Applications, World Scientific, Singapore, 1994.

M. L. Puri and D. A. Ralescu, “Fuzzy random variables,” Journal of Mathematical Analysis and Applications, vol. 114, no. 2, pp. 409–422, 1986.

Y. K. Kim, “Measurability for fuzzy valued functions,” Fuzzy Sets and Systems, vol. 129, no. 1, pp. 105–109, 2002.

V. Lakshmikantham, T. Gnana Bhaskar, and J. Vasundhara Devi, Theory of Set Differential Equations in Metric Spaces, Cambridge Scientific Publishers, Cambridge, UK, 2006.