- Research Article
- Open Access
Dynamic Analysis of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Delays
Advances in Difference Equations volume 2009, Article number: 410823 (2009)
Stochastic effects on convergence dynamics of reaction-diffusion Cohen-Grossberg neural networks (CGNNs) with delays are studied. By utilizing Poincaré inequality, constructing suitable Lyapunov functionals, and employing the method of stochastic analysis and nonnegative semimartingale convergence theorem, some sufficient conditions ensuring almost sure exponential stability and mean square exponential stability are derived. Diffusion term has played an important role in the sufficient conditions, which is a preeminent feature that distinguishes the present research from the previous. Two numerical examples and comparison are given to illustrate our results.
In the recent years, the problems of stability of delayed neural networks have received much attention due to its potential application in associative memories, pattern recognition and optimization. A large number of results have appeared in literature, see, for example, [1–14]. As is well known, a real system is usually affected by external perturbations which in many cases are of great uncertainty and hence may be treated as random [15–17]. As pointed out by Haykin  that in real nervous systems synaptic transmission is a noisy process brought on by random fluctuations from the release of neurotransmitters and other probabilistic causes, it is of significant importance to consider stochastic effects for neural networks. In recent years, the dynamic behavior of stochastic neural networks, especially the stability of stochastic neural networks, has become a hot study topic. Many interesting results on stochastic effects to the stability of delayed neural networks have been reported (see [16–23]).
In the factual operations, on other hand, diffusion phenomena could not be ignored in neural networks and electric circuits once electrons transport in a nonuniform electromagnetic field. Thus, it is essential to consider state variables varying with time and space variables. The delayed neural networks with diffusion terms can commonly be expressed by partial functional differential equation (PFDE). To study the stability of delayed reaction-diffusion neural networks, for instance, see [24–31], and references therein.
Based on the above discussion, it is significant and of prime importance to consider the stochastic effects on the stability property of the delayed reaction-diffusion networks. Recently, Sun et al. [32, 33] have studied the problem of the almost sure exponential stability and the moment exponential stability of an equilibrium solution for stochastic reaction-diffusion recurrent neural networks with continuously distributed delays and constant delays, respectively. Wan et al. have derived the sufficient condition of exponential stability of stochastic reaction-diffusion CGNNs with delay [34, 35]. In , the problem of stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters have been investigated. In [32–36], unfortunately, reaction-diffusion terms were omitted in the deductions, which result in that the criteria of obtained stability do not contain the diffusion terms. In other words, the diffusion terms do not take effect in their results. The same cases appear also in other research literatures on the stability of reaction-diffusion neural network [24–31].
Motivated by the above discussions, in this paper, we will further investigate the convergence dynamics of stochastic reaction-diffusion CGNNs with delays, where the activation functions are not necessarily bounded, monotonic, and differentiable. Utilizing Poincaré inequality and constructing appropriate Lyapunov functionals, some sufficient conditions on the almost surely and mean square exponential stability for the equilibrium point are established. The results show that diffusion terms have contributed to the almost surely and mean square exponential stability criteria. Two examples have been provided to illustrate the effectiveness of the obtained results.
The rest of this paper is organized as follows. In Section 2, a stochastic delayed reaction-diffusion CGNNs model is described, and some preliminaries are given. In Section 3, some sufficient conditions to guarantee the mean square and almost surely exponential stability of equilibrium point for the reaction-diffusion delayed CGNNs are derived. Examples and comparisons are given in Section 4. Finally, in Section 5, conclusions are given.
2. Model Description and Preliminaries
To begin with, we introduce some notations and recall some basic definitions and lemmas:
be an open bounded domain in with smooth boundary , and denotes the measure of . ;
is the space of real Lebesgue measurable functions on which is a Banach space for the -norm , ;
, where , . the closure of in ;
is the space of continuous functions which map into with the norm , for any ;
and be an -measurable -valued random variable, where, for example, restricted on , and be the Banach space of continuous and bounded functions with the norm , where , for any , ;
is the gradient operator, for . . is the Laplace operator, for .
Consider the following stochastic reaction-diffusion CGNNs with constant delays on :
where , corresponds to the number of units in a neural network; is a space variable, corresponds to the state of the th unit at time and in space ; corresponds to the transmission diffusion coefficient along the th neuron; represents an amplification function; is an appropriately behavior function; , denote the connection strengths of the th neuron on the th neuron, respectively; , denote the activation functions of th neuron at time and in space ; corresponds to the transmission delay and satisfies ( is a positive constant); is the constant input from outside of the network. Moreover, is an -dimensional Brownian motion defined on a complete probability space with the natural filtration generated by the process , where we associate with the canonical space generated by all , and denote by the associated -algebra generated by with the probability measure . The boundary condition is given by (Dirichlet type) or (Neumann type), where denotes the outward normal derivative on .
Model (2.1) includes the following reaction-diffusion recurrent neural networks (RNNs) as a special case:
When for any , model (2.1) also comprises the following reaction-diffusion CGNNs with no stochastic effects on space :
Throughout this paper, we assume that
each function is bounded, positive and continuous, that is, there exist constants , such that , for , ,
and , for ,
, are bounded, and , , are Lipschitz continuous with Lipschitz constant , , , for ,
, for .
Using the similar method of , it is easily to prove that under assumptions (H1)–(H3), model (2.3) has a unique equilibrium point which satisfies
Suppose that system (2.1) satisfies assumptions (H1)–(H4), then equilibrium point of model (2.3) is also a unique equilibrium point of system (2.1).
By the theory of stochastic differential equations, see [15, 37], it is known that under the conditions (H1)–(H4), model (2.1) has a global solution denoted by or simply , or , if no confusion should occur. For the effects of stochastic forces to the stability property of delayed CGNNs model (2.1), we will study the almost sure exponential stability and the mean square exponential stability of their equilibrium solution in the following sections. For completeness, we give the following definitions , in which denotes expectation with respect to .
The equilibrium solution of model (2.1) is said to be almost surely exponentially stable, if there exists a positive constant such that for any there is a finite positive random variable such that
The equilibrium solution of model (2.1) is said to be th moment exponentially stable, if there exists a pair of positive constants and such that for any ,
When and , it is usually called the exponential stability in mean value and mean square, respectively.
The following lemmas are important in our approach.
Lemma 2.3 (nonnegative semimartingale convergence theorem ).
Suppose and are two continuous adapted increasing processes on with , a.s. Let be a real-valued continuous local martingale with , a.s. and let be a nonnegative -measurable random variable with . Define for . If is nonnegative, then
where a.s. denotes . In particular, if a.s., then for almost all and , that is, both and converge to finite random variables.
Lemma 2.4 (Poincaré inequality).
Let be a bounded domain of with a smooth boundary of class by . is a real-valued function belonging to and satisfies . Then
which is the lowest positive eigenvalue of the boundary value problem
We just give a simple sketch here.
Under the Neumann boundary condition, that is, . According to the eigenvalue theory of elliptic operators, the Laplacian on with the Neumann boundary conditions is a self-adjoint operator with compact inverse, so there exists a sequence of nonnegative eigenvalues (going to ) and a sequence of corresponding eigenfunctions, which are denoted by and , respectively. In other words, we have
where . Multiply the second equation of (2.10) by and integrate over . By Green's theorem, we obtain
Clearly, (2.11) can also hold for . The sequence of eigenfunctions defines an orthonormal basis of . For any , we have
From (2.11) and (2.12), we can obtain
Under the Dirichlet boundary condition, that is, . By the same may, we can obtained the inequality.
This completes the proof.
Remark 2.5. (i) The lowest positive eigenvalue in the boundary problem (2.9) is sometimes known as the first eigenvalue. (ii) The magnitude of is determined by domain . For example, let Laplacian on , if and , respectively, then and [38, 39]. (iii) Although the eigenvalue of the laplacian with the Dirichlet boundary condition on a generally bounded domain cannot be determined exactly, a lower bound of it may nevertheless be estimated by , where is a surface area of the unit ball in , is a volume of domain .
In Section 4, we will compare the results between this paper and previous literatures. To this end, we recall some previous results as follows (according to the symbols in this paper).
Suppose that system (2.1) satisfies the assumptions (H1)–(H4) and
, , where , , , , , , , . Also, denotes the spectral radius of a square matrix .
Then model (2.1) is mean value exponentially stable.
It should be noted that condition (A) in Proposition 2.6 is equivalent to is a nonsingular -matrix, where . Thus, the following result is treated as a special case of Proposition 2.6.
Proposition 2.8 (see [33, Theorem 3.1]).
Suppose that model (2.2) satisfies the assumptions (H2)–(H4) and
is a nonsingular -matrix, where , , for .
Then model (2.2) is almost surely exponentially stable.
It is obvious that conditions (A) and (B) are irrelevant to the diffusion term. In other words, the diffusion term does not take effect in Propositions 2.6 and 2.8.
3. Main Results
Under assumptions (H1)–(H4), if the following conditions hold:
, for any ,
where is the lowest positive eigenvalue of problem (2.9), , .
Then model (2.1) is almost surely exponentially stable.
Let be an any solution of model (2.1) and . Model (2.1) is equivalent to
It follows from (H5) that there exists a sufficiently small constant such that
To derive the almost surely exponential stability result, we construct the following Lyapunov functional:
By Itô's formula to along (3.1a), we obtain
By the boundary condition, it is easy to calculate that
From assumptions (H1) and (H2), we have
From assumptions (H1) and (H3), we have
By the same way, we can obtain
Combining (3.6)–(3.9) into (3.5), we get
It is obvious that the right-hand side of (3.6) is a nonnegative semimartingale. From Lemma 2.3, it is easy to see that its limit is finite almost surely as , which shows that
The proof is complete.
Under the conditions of Theorem 3.1, model (2.1) is mean square exponentially stable.
Taking expectations on both sides of (3.11) and noticing that
By (3.17)–(3.20), we have
Then, we easily get
The proof is completed.
By the similar way of the proof of Theorem 3.1, it is easy to prove the following results.
Under assumptions (H2)–(H4), if the following conditions hold:
Then model (2.2) is almost surely exponentially stable and mean square exponentially stable.
In the proof of Theorem 3.1, by Poincaré inequality, we have obtained (see (3.6)). This is an important step that results in the condition of Theorem 3.1 including the diffusion terms.
It should be noted that assumptions (H5) and (H6) allow
respectively, which cannot guarantee the mean square exponential stability of the equilibrium solution of models (2.1) and (2.2). Thus, as we can see form Theorems 3.1, 3.2, and 3.3, reaction-diffusion terms do contribute the almost surely exponential stability and the mean square exponential stability of models (2.1) and (2.2), respectively. However, as we can see from Propositions 2.6 and 2.8, the diffusion term do not take effect in the convergence dynamics of delayed stochastic reaction-diffusion neural networks. Thus, the criteria what we proposed are less conservative and restrictive than Propositions 2.6 and 2.8.
Under assumptions (H1)–(H3), if
, for any ,
holds, the equilibrium point of system (2.2) is globally exponentially stable.
Theorem 3.6 shows that the globally exponential stability criteria on reaction-diffusion CGNNs with delays depend on the diffusion term. In exact words, diffusion terms have contributed to exponentially stabilization of reaction-diffusion CGNNs with delays. It should be noted that the authors in [24–28] have studied reaction-diffusion neural networks (including CGNNs and RNNs) with delays and obtained the sufficient condition of exponential stability. However, those sufficient condition are independent of the diffusion term. Obviously, the criteria what we proposed are less conservative and restrictive than those in [24–28].
4. Examples and Comparison
In order to illustrate the feasibility of our above established criteria in the preceding sections, we provide two concrete examples. Although the selection of the coefficients and functions in the examples is somewhat artificial, the possible application of our theoretical theory is clearly expressed.
Consider the following stochastic reaction-diffusion neural networks model on
where . It is clear that , , , . According to Remark 2.5, we can get . Taking
It follows from Theorem 3.3 that the equilibrium solution of such system is almost surely exponentially stable and mean square exponential stable.
It should be noted that
it is well known, which cannot guarantee the mean square exponential stability of the equilibrium solution of model (4.1). Thus, as we can see in Example 4.1, the reaction-diffusion terms have contributed to the almost surely and mean square exponential stability of this model.
For the model (4.1), the diffusion operator, space , and the Neumann boundary conditions are replaced by,
and the Dirichlet boundary condition
respectively. The remainder parameters and functions unchanged. According to Remark 2.5, we see that . By the same way of Example 4.1, equilibrium solution of model (4.5) is almost surely exponentially stable and mean square.
Now, we compare the results in this paper with Propositions 2.6 and 2.8.
The authors in [33, 35] have considered the stochastic delayed reaction-diffusion neural networks with Neumann boundary condition and obtained the sufficient conditions to guarantee the almost surely or mean value exponential stability. We notice that the conditions of Propositions 2.6 and 2.8 do not include the diffusion terms, hence, in principal, Propositions 2.6 and 2.8 could be applied to analyze the exponential stability of stochastic system (4.1), but could not be model (4.5) for its the Dirichlet boundary condition. Unfortunately, Propositions 2.6 and 2.8 are not applicable to ascertain the exponential stability of model (4.1).
In fact, it is easy to calculate that
That is, condition (A) of Proposition 2.6 does not hold.
Next, we explain that Proposition 2.8 is not applicable to ascertain the almost surely exponential stability of system (4.1):
is not a nonsingular -matrix. This implies that condition (A) of Proposition 2.6 is not satisfied.
The above comparison shows that reaction-diffusion term contributes to the exponentially stabilization of a stochastic reaction-diffusion neural network and the previous results have been improved.
The problem of the convergence dynamics for the stochastic reaction-diffusion CGNNs with delays has been studied in this paper. This neural networks is quite general, and can be used to describe some well-known neural networks, including Hopfield neural networks, cellular neural networks, and generalized CGNNs. By Poincaré inequality and constructing suitable Lyapunov functional, we obtain some sufficient condition to ensure the almost sure and mean square exponential stability of the system. It is worth noting that the diffusion term has played an important role in the obtained conditions, a significant feature that distinguishes the results in this paper from the previous. Two examples are given to show the effectiveness of the results. Moreover, the methods in this paper can been used to consider other stochastic delayed reaction-diffusion neural network model with the Neumann or Dirichlet boundary condition.
Arik S, Orman Z: Global stability analysis of Cohen-Grossberg neural networks with time varying delays. Physics Letters A 2005,341(5-6):410-421. 10.1016/j.physleta.2005.04.095
Chen Z, Ruan J: Global stability analysis of impulsive Cohen-Grossberg neural networks with delay. Physics Letters A 2005,345(1–3):101-111.
Chen Z, Ruan J: Global dynamic analysis of general Cohen-Grossberg neural networks with impulse. Chaos, Solitons & Fractals 2007,32(5):1830-1837. 10.1016/j.chaos.2005.12.018
Cohen MA, Grossberg S: Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Transactions on Systems, Man, and Cybernetics 1983,13(5):815-826.
Huang T, Chan A, Huang Y, Cao J: Stability of Cohen-Grossberg neural networks with time-varying delays. Neural Networks 2007,20(8):868-873. 10.1016/j.neunet.2007.07.005
Liao X, Li C, Wong K-W: Criteria for exponential stability of Cohen-Grossberg neural networks. Neural Networks 2004,17(10):1401-1414. 10.1016/j.neunet.2004.08.007
Liu X, Wang Q: Impulsive stabilization of high-order hopfield-type neural networks with time-varying delays. IEEE Transactions on Neural Networks 2008,19(1):71-79.
Yang Z, Xu D: Impulsive effects on stability of Cohen-Grossberg neural networks with variable delays. Applied Mathematics and Computation 2006,177(1):63-78. 10.1016/j.amc.2005.10.032
Zhang J, Suda Y, Komine H: Global exponential stability of Cohen-Grossberg neural networks with variable delays. Physics Letters A 2005,338(1):44-50. 10.1016/j.physleta.2005.02.005
Zhou Q: Global exponential stability for a class of impulsive integro-differential equation. International Journal of Bifurcation and Chaos 2008,18(3):735-743. 10.1142/S0218127408020616
Park JH, Kwon OM: Synchronization of neural networks of neutral type with stochastic perturbation. Modern Physics Letters B 2009,23(14):1743-1751. 10.1142/S0217984909019909
Park JH, Kwon OM: Delay-dependent stability criterion for bidirectional associative memory neural networks with interval time-varying delays. Modern Physics Letters B 2009,23(1):35-46. 10.1142/S0217984909017807
Park JH, Kwon OM, Lee SM: LMI optimization approach on stability for delayed neural networks of neutral-type. Applied Mathematics and Computation 2008,196(1):236-244. 10.1016/j.amc.2007.05.047
Meng Y, Guo S, Huang L: Convergence dynamics of Cohen-Grossberg neural networks with continuously distributed delays. Applied Mathematics and Computation 2008,202(1-2):188-199.
Arnold L: Stochastic Differential Equations: Theory and Applications. John Wiley & Sons, New York, NY, USA; 1972.
Blythe S, Mao X, Liao X: Stability of stochastic delay neural networks. Journal of the Franklin Institute 2001,338(4):481-495. 10.1016/S0016-0032(01)00016-3
Buhmann J, Schulten K: Influence of noise on the function of a "physiological" neural network. Biological Cybernetics 1987,56(5-6):313-327. 10.1007/BF00319512
Haykin S: Neural Networks. Prentice-Hall, Upper Saddle River, NJ, USA; 1994.
Sun Y, Cao J:th moment exponential stability of stochastic recurrent neural networks with time-varying delays. Nonlinear Analysis: Real World Applications 2007,8(4):1171-1185. 10.1016/j.nonrwa.2006.06.009
Wan L, Sun J: Mean square exponential stability of stochastic delayed Hopfield neural networks. Physics Letters A 2005,343(4):306-318. 10.1016/j.physleta.2005.06.024
Wan L, Zhou Q: Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays. Physics Letters A 2007,370(5-6):423-432. 10.1016/j.physleta.2007.05.095
Zhao H, Ding N: Dynamic analysis of stochastic bidirectional associative memory neural networks with delays. Chaos, Solitons & Fractals 2007,32(5):1692-1702. 10.1016/j.chaos.2005.12.010
Zhou Q, Wan L: Exponential stability of stochastic delayed Hopfield neural networks. Applied Mathematics and Computation 2008,199(1):84-89. 10.1016/j.amc.2007.09.025
Zhao H, Ding N: Dynamic analysis of stochastic Cohen-Grossberg neural networks with time delays. Applied Mathematics and Computation 2006,183(1):464-470. 10.1016/j.amc.2006.05.087
Song Q, Cao J: Global exponential robust stability of Cohen-Grossberg neural network with time-varying delays and reaction-diffusion terms. Journal of the Franklin Institute 2006,343(7):705-719. 10.1016/j.jfranklin.2006.07.001
Song Q, Cao J: Exponential stability for impulsive BAM neural networks with time-varying delays and reaction-diffusion terms. Advances in Difference Equations 2007, 2007:-18.
Liang J, Cao J: Global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays. Physics Letters A 2003,314(5-6):434-442. 10.1016/S0375-9601(03)00945-9
Wang L, Xu D: Global exponential stability of Hopfield reaction-diffusion neural networks with time-varying delays. Science in China. Series F 2003,46(6):466-474. 10.1360/02yf0146
Yang J, Zhong S, Luo W: Mean square stability analysis of impulsive stochastic differential equations with delays. Journal of Computational and Applied Mathematics 2008,216(2):474-483. 10.1016/j.cam.2007.05.022
Zhao H, Wang K: Dynamical behaviors of Cohen-Grossberg neural networks with delays and reaction-diffusion terms. Neurocomputing 2006,70(1–3):536-543.
Zhou Q, Wan L, Sun J: Exponential stability of reaction-diffusion generalized Cohen-Grossberg neural networks with time-varying delays. Chaos, Solitons & Fractals 2007,32(5):1713-1719. 10.1016/j.chaos.2005.12.003
Lv Y, Lv W, Sun J: Convergence dynamics of stochastic reaction-diffusion recurrent neural networks in continuously distributed delays. Nonlinear Analysis: Real World Applications 2008,9(4):1590-1606. 10.1016/j.nonrwa.2007.04.003
Sun J, Wan L: Convergence dynamics of stochastic reaction-diffusion recurrent neural networks with delays. International Journal of Bifurcation and Chaos 2005,15(7):2131-2144. 10.1142/S0218127405013332
Wan L, Zhou Q, Sun J: Mean value exponential stability of stochastic reaction-diffusion generalized Cohen-Grossberg neural networks with time-varying delay. International Journal of Bifurcation and Chaos 2007,17(9):3219-3227. 10.1142/S021812740701897X
Wan L, Zhou Q: Exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with delays. Applied Mathematics and Computation 2008,206(2):818-824. 10.1016/j.amc.2008.10.002
Wang L, Zhang Z, Wang Y: Stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters. Physics Letters A 2008,372(18):3201-3209. 10.1016/j.physleta.2007.07.090
Mao X: Stochastic Differential Equations and Applications. Horwood, Chichester, UK; 1997.
Temam R: Infinite-Dimensional Dynamical Systems in Mechanics and Physics, Applied Mathematical Sciences. Volume 68. Springer, New York, NY, USA; 1988:xvi+500.
Ye Q, Li Z: Introduction of Reaction-Diffusion Equation. Science Press, Beijing, China; 1999.
Niu P, Qu J, Han J: Estimation of the eigenvalue of Laplace operator and generalization. Journal of Baoji College of Arts and Science. Natural Science 2003,23(1):85-87.
The authors would like to thank the editor and the reviewers for their detailed comments and valuable suggestions which have led to a much improved paper. This paper is supported by National Basic Research Program of China (2010CB732501).
About this article
Cite this article
Pan, J., Zhong, S. Dynamic Analysis of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Delays. Adv Differ Equ 2009, 410823 (2009). https://doi.org/10.1155/2009/410823
- Dirichlet Boundary Condition
- Equilibrium Solution
- Exponential Stability
- Neumann Boundary Condition
- Diffusion Term