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Stochastic Stability Analysis for Stochastic Coupled Oscillator Networks with Bidirectional Cross-Dispersal
It is well known that stochastic coupled oscillator network (SCON) has been widely applied; however, there are few studies on SCON with bidirectional cross-dispersal (SCONBC). This paper intends to study stochastic stability for SCONBC. A new and suitable Lyapunov function for SCONBC is constructed on the basis of Kirchhoff's matrix tree theorem in graph theory. Combining stochastic analysis skills and Lyapunov method, a sufficient criterion guaranteeing stochastic stability for the trivial solution of SCONBC is provided, which is associated with topological structure and coupling strength of SCONBC. Furthermore, some numerical simulation examples are given in order to illustrate the validity and practicability of our results.
In the past few decades, stochastic coupled oscillator network (SCON) has attracted extensive attention from the scientific community and has been widely used in many fields, such as physics [1–3], biology [4, 5], engineering [6, 7], and so on. On the other hand, dispersal is a common phenomenon in nature, which is due to the imbalance of oscillators in different regions. A lot of results about single dispersal have appeared in [8–10] since it plays an important role in the research of application problems. In addition, it is worth noting that dispersal also occurs between different oscillators of different groups, that is, bidirectional cross-dispersal. To the best of the authors’ knowledge, SCON with bidirectional cross-dispersal (SCONBC) is rarely studied. Based on the above discussion, the purpose of this paper is to research the stochastic stability of SCONBC.
As is known to all, the Lyapunov method is a powerful tool for analyzing the stochastic stability of SCONBC. For all that, owing to the complex structure of stochastic coupled oscillator networks with bidirectional cross-dispersal terms, it is quite challenging to construct the Lyapunov function for SCONBC. Li et al. proposed a method to solve this problem by combining graph theory in . In this paper, inspired by them, we successfully construct a suitable Lyapunov function for SCONBC by the approach combining Kirchhoff's matrix tree theorem in graph theory, which solves the above problem we mentioned and has been applied in various articles [12, 13].
Gao et al. has researched periodic solutions for neutral coupled oscillator network with feedback and time-varying delay and the existence of periodic solutions for discrete-time coupled systems on networks with time-varying delay in [14, 15]. Compared with the existing literature, our innovations and contributions are as follows:(1)Bidirectional cross-dispersal terms are taken into SCONBC, and a new Lyapunov function for SCONBC is constructed by applying Kirchhoff's matrix tree theorem in graph theory.(2)A sufficient criterion is obtained, which combines stochastic analysis skills and can show how topological structure and coupling strength affect the stochastic stability for the trivial solution of SCONBC.(3)Some numerical examples and their simulation results are provided to validate the applicability of our theoretical results.
The structure of this paper is arranged as follows. Some necessary notations are given in Section 2.1, and concepts about graph theory are provided in Section 2.2. In Section 3, we establish SCONBC and give its model formulation. A sufficient criterion ensuring stochastic stability for the trivial solution of SCONBC and its proof is offered in Section 4. Section 5 provides some numerical simulation examples. Finally, the conclusion is drawn in Section 6.
Throughout this paper, the notations in Table 1 will be used unless otherwise specified.
Other notations will be explained where they first appear.
2.2. Graph Theory
Here, we introduce some useful concepts associated with graph theory. A digraph contains a set of vertices and a set of arcs which lead from initial vertex to terminal vertex , and each vertex of digraph is regarded as an oscillator. Define the weight matrix of as , where if there exists an arc from vertex to vertex . Digraph with the weighted matrix is denoted by . The Laplacian matrix of digraph is defined as
For other details on graph theory, we refer the readers to [16, 17].
At the end of this section, we provide a lemma in graph theory.
Lemma 1. (see ) (Kirchhoff’s matrix tree theorem). Assume that Let denote the cofactor of the -th diagonal element of the Laplacian matrix of the weighted digraph Then,where is the set of all spanning trees of the weighted digraph that are rooted at vertex and is the weight of . Particularly, if the weighted digraph is strongly connected, then .
3. Model Formulation
Stochastic oscillators have important applications in many branches of industry , such as biology , physics , and so on. In this section, we provide a detailed description of SCONBC. Let us firstly see the stochastic oscillator equation with white noise, which is expressed aswhere is the system state, and are damping coefficients, and is a one-dimensional Brownian motion. In this paper, we consider oscillators and the -th oscillator is denoted as follows:where denotes the system state of the -th oscillator. Since the bidirectional cross-dispersal is a common phenomenon in our real life, in order to describe the dynamic behavior of system (3) more accurately, the bidirectional cross-dispersal terms are added, and based on a transform of , system (4) can be written as follows:where the dispersal of in the -th group of oscillators which is from in the -th group of oscillators is expressed as the function and the function denotes the dispersal of in the -th group of oscillators which is from in the -th group of oscillators. and represent the coupling strength of the irreducible coupling configuration matrices and separately. Specially, it is worth noting that and = 0 if there is no cross-dispersal from in the -th group of oscillators to in the -th group of oscillators and from in the -th group of oscillators to in the -th group of oscillators.
The form of SCONBC (5) is too complex for the readers to read and will make subsequent proof tedious; therefore, we solve these problems by simplifying SCONBC (5) into the following SCONBC (6):where
We let the initial value , and it is easy to see that there exists a trivial solution denoted as to SCONBC (4).
Subsequently, for any a differential operator of SCONBC (4) is normally defined by 
Our purpose is to explore the stochastic stability for the trivial solution of SCONBC (4) in this paper, and its definition is given as follows.
Definition 1. If for every and the constant , there exists such thatfor the initial value , then the trivial solution of SCONBC (3) is stochastically stable.
4. Main Results
In this section, we will provide a theorem and its proof in regard to the stochastic stability for the trivial solution of SCONBC (4).
Theorem 1. The trivial solution of SCONBC (4) is stochastically stable if the following condition is satisfied for any where and are the cofactors of the -th diagonal element of the Laplacian matrix in digraphs and , respectively.
Proof. We firstly construct a Lyapunov function as follows:where in light of Lemma 1.
According to the differential operator defined above, it can be obtained thatwhere and In accordance with combination identical equation in graph theory (see , Theorem 2.2) and the fact as well as , we can know thatwhere and are the sets of all spanning unicyclic graphs of the weighted digraphs and , respectively, and are the weights of and separately, and and , respectively, denote the directed cycle of as well as . Then, taking inequalities (14) and (15) into inequality (1, we can getOn the other hand, let , where is a positive constant and we can getwhere and It is obvious that is a continuous positive definite function and . Hence, for every and the constant , we can find a such thatwhere . Then, it can be easily obtained that . Subsequently, we let and establish a stopping time sequence where
Based on ’s formula, we can obtainTaking the expectation of both sides of equality (19) and on the basis of inequality (16), it is derived thatWhen , in conformity with inequality (17), we can getAccording to inequalities (20) and (21) and δ that we find above, it can be obtained thatLet , and we can getTherefore,which means that the trivial solution of SCONBC (4) is stochastically stable according to Definition 1.
Remark 1. The condition in Theorem 1 is mild and reflects the close relationship between the stochastic stability for the trivial solution of SCONBC (4) and the topological structure of digraphs and . In addition, due to the high dimension as well as the complex structure of SCONBC (4), it is obviously difficult to establish a suitable Lyapunov function for SCONBC (4). In this paper, we propose a framework method to solve this problem, that is, constructing Lyapunov function by Kirchhoff's matrix tree theorem in graph theory, and the method can be applied to more complex network models.
Remark 2. In recent years, the dynamic behavior of stochastic coupled oscillator networks has been widely studied and applied. Zhang et.al researched the exponential synchronization problem of stochastic coupled oscillator networks with time-varying delays in . In , Li et al. illustrated the synchronous stationary distribution of hybrid stochastic coupled oscillator networks. Different from the above results, this paper explores stochastic coupled oscillators with bidirectional cross-dispersal terms, which makes the study of SCONBC (4) more practical.
5. Numerical Test
In this part, some numerical simulation examples are provided to verify the validity of our results. Here, we consider SCONBC (4) with oscillators and let positive constants
The coupling configuration matrices arewhich are irreducible evidently. By calculation, we can get thatwhich means that the condition in Theorem 1 is met. Therefore, SCONBC (4) is stochastically stable, whose dynamic behavior can be seen in Figures 1–3 with the initial values as follows:
In this paper, we have researched stochastic stability for the trivial solution of SCONBC (4). Based on Kirchhoff's matrix tree theorem in graph theory, a new and suitable Lyapunov function is constructed. A sufficient criterion which ensures that the trivial solution of SCONBC (4) is stochastically stable has been given by applying stochastic analysis skills and Lyapunov method. Finally, some numerical simulation examples have been presented to explain the validity of our theories. Compared with the stochastic coupled oscillator networks studied in the previous papers [14, 15], this paper considers the bidirectional cross-dispersal terms. Due to the phenomenon of bidirectional cross-dispersal between different oscillators in different groups, our results can be widely used in the study of biological populations, the interaction of physical oscillators, and so on. However, this paper has considered the small noise in real life, namely, white noise, but in real life, there are many colored noises such as Levy noise, Poisson noise, second moment process noise, and so on, which is the limitation of this article and our future research work.
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
This study was supported by the Fundamental Research Funds for the Central Universities (no. 2572019BC11).
P. Li and Z. Yi, “Synchronization of Kuramoto oscillators in random complex networks,” Physica A: Statistical Mechanics and Its Applications, vol. 387, no. 7, pp. 1669–1674, 2008.View at: Publisher Site | Google Scholar
Y. Liu, P. Yu, D. Chu, and H. Su, “Stationary distribution of stochastic Markov jump coupled systems based on graph theory,” Chaos, Solitons & Fractals, vol. 119, pp. 188–195, 2019.View at: Publisher Site | Google Scholar
A. Pototsky and N. B. Janson, “Delay-induced spatial correlations in one-dimensional stochastic networks with nearest-neighbor coupling,” Physical Review A, vol. 80, no. 6, p. 066203, 2009.View at: Publisher Site | Google Scholar
N. Bagheri, S. R. Taylor, K. Meeker, L. R. Petzold, and F. J. Doyle III, “Synchrony and entrainment properties of robust circadian oscillators,” Journal of The Royal Society Interface, vol. 5, no. suppl_1, pp. S17–S28, 2008.View at: Publisher Site | Google Scholar
M. N. Lorenzo, N. Montejo, V. Pérez-Muñuzuri, and V. Pérez-Villar, “Spatiotemporal behavior in networks of Ca3 region in the hippocampus,” Neurocomputing, vol. 58-60, pp. 705–712, 2004.View at: Publisher Site | Google Scholar
J. Liu, K. Feng, Y. Qu, A. H. Nawaz, and F. Wang, “Stability analysis of T-S fuzzy coupled oscillator systems influenced by stochastic disturbance,” Neural Computing & Applications, vol. 33, no. 7, pp. 2549–2560, 2021.View at: Publisher Site | Google Scholar
B.-S. Chen and C.-Y. Hsu, “Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing,” BMC Systems Biology, vol. 6, no. 1, pp. 136–215, 2012.View at: Publisher Site | Google Scholar
L. Liu, W. Cai, and Y. Wu, “Global dynamics for an SIR patchy model with suspectibles dispersal,” Advances in Differential Equations, vol. 131, pp. 1–11, 2012.View at: Google Scholar
W. Wang and X.-Q. Zhao, “An epidemic model in a patchy environment,” Mathematical Biosciences, vol. 190, no. 1, pp. 97–112, 2004.View at: Publisher Site | Google Scholar
T. Chen, Z. Sun, and B. Wu, “Stability of multi-group models with cross-dispersal based on graph theory,” Applied Mathematical Modelling, vol. 47, pp. 745–754, 2017.View at: Publisher Site | Google Scholar
M. Y. Li and Z. Shuai, “Global-stability problem for coupled systems of differential equations on networks,” Journal of Differential Equations, vol. 248, no. 1, pp. 1–20, 2010.View at: Publisher Site | Google Scholar
S. Gao, C. Peng, J. Li, R. Kang, X. Liu, and C. Zhang, “Global asymptotic stability in mean for stochastic complex networked control systems,” Communications in Nonlinear Science and Numerical Simulation, vol. 107, p. 106162, 2022.View at: Publisher Site | Google Scholar
C. Zhang, W. Li, and K. Wang, “A graph-theoretic approach to stability of neutral stochastic coupled oscillators network with time-varying delayed coupling,” Mathematical Methods in the Applied Sciences, vol. 37, no. 8, pp. 1179–1190, 2014.View at: Publisher Site | Google Scholar
S. Gao, H. Zhou, and B. Wu, “Periodic solutions for neutral coupled oscillators network with feedback and time-varying delay,” Applicable Analysis, vol. 96, no. 12, pp. 1983–2001, 2017.View at: Publisher Site | Google Scholar
S. Gao, H. Guo, and T. Chen, “The existence of periodic solutions for discrete-time coupled systems on networks with time-varying delay,” Physica A: Statistical Mechanics and Its Applications, vol. 526, p. 120876, 2019.View at: Publisher Site | Google Scholar
D. B. West, Introduction to Graph Theory, Prentice-Hall, Upper Saddle River, 2001.
N. Biggs, E. K. Lloyd, and R. J. Wilson, Graph Theory, 1736-1936, Oxford University Press, Oxford, United Kingdom, 1986.
X. R. Mao, Stochastic Differential Equations and Application, Horwood, Bristol, United Kingdom, 2007.
Y. C. Lai and K. Park, “Noise-sensitive measure for stochastic resonance in biological oscillators,” Mathematical Biosciences and Engineering, vol. 3, no. 4, pp. 583–602, 2006.View at: Publisher Site | Google Scholar
M. L. Zuparic and A. C. Kalloniatis, “Stochastic (in)stability of synchronisation of oscillators on networks,” Physica D: Nonlinear Phenomena, vol. 255, pp. 35–51, 2013.View at: Publisher Site | Google Scholar
C. Zhang, W. Li, and K. Wang, “Exponential synchronization of stochastic coupled oscillators networks with delays,” Applicable Analysis, vol. 96, no. 6, pp. 1058–1075, 2017.View at: Publisher Site | Google Scholar
S. Li, H. Su, and X. Ding, “Synchronized stationary distribution of hybrid stochastic coupled systems with applications to coupled oscillators and a Chua's circuits network,” Journal of the Franklin Institute, vol. 355, no. 17, pp. 8743–8765, 2018.View at: Publisher Site | Google Scholar