Abstract
In this paper, both global exponential stability and periodic solutions are investigated for a class of delayed reactiondiffusion BAM neural networks with Dirichlet boundary conditions. By employing suitable Lyapunov functionals, sufficient conditions of the global exponential stability and the existence of periodic solutions are established for reactiondiffusion BAM neural networks with mixed time delays and Dirichlet boundary conditions. The derived criteria extend and improve previous results in the literature. A numerical example is given to show the effectiveness of the obtained results.
Keywords:
neural networks; reactiondiffusion; mixed time delays; global exponential stability; Poincaré mapping; Lyapunov functional1 Introduction
Neural networks (NNs) have been extensively studied in the past few years and have found many applications in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. Delayed versions of NNs were also proved to be important for solving certain classes of motionrelated optimization problems. Various results concerning the dynamical behavior of NNs with delays have been reported during the last decade (see, e.g., [17]). Recently, the authors in [1] and [2] considered the problem of exponential passivity analysis for uncertain NNs with timevarying delays and passivitybased controller design for Hopfield NNs, respectively.
Since NNs related to bidirectional associative memory (BAM) were proposed by Kosko [8], the BAM NNs have been one of the most interesting research topics and have attracted the attention of researchers. In the design and applications of networks, the stability of the designed BAM NNs is one of the most important issues (see, e.g., [912]). Many important results concerning mainly the existence and stability of equilibrium of BAM NNs have been obtained (see, e.g., [915]).
However, strictly speaking, diffusion effects cannot be avoided in the NNs when electrons are moving in asymmetric electromagnetic fields. So, we must consider that the activations vary in space as well as in time. In [1634], the authors considered the stability of NNs with diffusion terms which were expressed by partial differential equations. In particular, the existence and attractivity of periodic solutions for nonautonomous reactiondiffusion CohenGrossberg NNs with discrete time delays were investigated in [20]. The authors derived sufficient conditions on the stability and periodic solutions of delayed reactiondiffusion NNs (RDNNs) with Neumann boundary conditions in [2125]. In these works, due to the divergence theorem employed, a negative integral term with gradient was removed in their deduction. Therefore, the stability criteria acquired by them do not contain diffusion terms; that is to say, the diffusion terms do not have any effect on their deduction and results. Meanwhile, some conditions dependent on the diffusion coefficients were given in [30,3234] to ensure the global exponential stability and periodicity of RDNNs with Dirichlet boundary conditions based on 2norm.
To the best of our knowledge, there are few reports about global exponential stability and periodicity of RDNNs with mixed time delays and Dirichlet boundary conditions, which are very important in theories and applications and also are a very challenging problem. In this paper, by employing suitable Lyapunov functionals, we shall apply inequality techniques to establish global exponential stability criteria of the equilibrium and periodic solutions for RDNNs with mixed time delays and Dirichlet boundary conditions. The derived criteria extend and improve previous results in the literature [22,29].
Throughout this paper, we need the following notations. denotes the ndimensional Euclidean space. We denote
and
The remainder of this paper is organized as follows. In Section 2, the basic notations, model description and assumptions are introduced. In Sections 3 and 4, criteria are proposed to determine global exponential stability, and periodic solutions are considered for reactiondiffusion recurrent neural networks with mixed time delays, respectively. An illustrative example is given to illustrate the effectiveness of the obtained results in Section 5. We also conclude this paper in Section 6.
2 Model description and preliminaries
In this paper, the RDNNs with mixed time delays are described as follows:
The RDNNs model given in (1) can be regarded as RDNNs with two layers, where m is the number of neurons in the first layer and n is the number of neurons in the second layer. , Ω is a compact set with smooth boundary ∂Ω and in the space ; , . and represent the state of the ith neuron in the first layer and the jth neuron in the second layer at time t and in the space x, respectively. , , , , and are known constants denoting the synaptic connection strengths between the neurons in the two layers, respectively; , , , , and denote the activation functions of the neurons and the signal propagation functions, respectively. and denote the external inputs on the ith neuron and jth neuron, respectively; and are differentiable real functions with positive derivatives defining the neuron charging time; and represent continuous timevarying delay and discrete delay, respectively; and , , and , stand for the transmission diffusion coefficient along the ith neuron and jth neuron, respectively.
System (1) is supplemented with the following boundary conditions and initial values:
for any and , where is the outer normal vector of ∂Ω, are bounded and continuous, where . It is the Banach space of continuous functions which maps into with the topology of uniform convergence for the norm
Remark 1 Some famous NN models became a special case of system (1). For example, when and (, ), the special case of model (1) is the model which has been studied in [1315]. When and , , , system (1) became NNs with distributed delays and reactiondiffusion terms [18,22,29].
Throughout this paper, we assume that the following conditions are made.
(A1) The functions , are piecewisecontinuous of class on the closure of each continuity subinterval and satisfy
with some constants , , , for all .
(A2) The functions and are piecewisecontinuous of class on the closure of each continuity subinterval and satisfy
(A3) The activation functions and the signal propagation functions are bounded and Lipschitz continuous, i.e., there exist positive constants , , , , and such that for all ,
(A4) The delay kernels (, ) are realvalued nonnegative continuous functions that satisfy the following conditions:
(iii) There exist a positive μ such that
Let be the equilibrium point of system (1).
Definition 1 The equilibrium point of system (1) is said to be globally exponentially stable if we can find such that there exist constants and such that
Remark 2 It is well known that bounded activation functions always guarantee the existence of an equilibrium point for system (1).
Lemma 1[33]
Let Ω be a cube (), and letbe a realvalued function belonging towhich vanishes on the boundary∂Ω of Ω, i.e., . Then
3 Global exponential stability
Now we are in a position to investigate the global exponential stability of system (1). By constructing a suitable Lyapunov functional, we arrive at the following conclusion.
Theorem 1Let (A1)(A4) be in force. If there exist (), , , such that
and
in which, , , , , , andare Lipschitz constants, , , then the equilibrium pointof system (1) is unique and globally exponentially stable.
Proof If (6) holds, we can always choose a positive number (may be very small) such that
and
Let us consider the functions
and
From (8) and (A4), we derive , ; and are continuous for . Moreover, as and as . Thus there exist constants such that
and
and
Suppose is any solution of model (1). Rewrite model (1) as
Multiplying (11) by and integrating over Ω yield
According to Green’s formula and the Dirichlet boundary condition, we get
Moreover from Lemma 1, we have
From (11)(15), (A2), (A3) and the Holder integral inequality, we obtain that
Multiplying both sides of (12) by , similarly, we also have
Choose a Lyapunov functional as follows:
Its upper Diniderivative along the solution to system (1) can be calculated as follows:
From (18) and the Young inequality, we can conclude
From (6), we can conclude
Since
Noting that
Let
It follows that
for any , where is a constant. This implies that the solution of (1) is globally exponentially stable. This completes the proof of Theorem 1. □
Remark 3 In this paper, the derived sufficient condition includes diffusion terms. Unfortunately, in the proof in the previous papers [2124], a negative integral term with gradient is left out in their deduction. This leads to the fact that those criteria are irrelevant to the diffusion term. Obviously, Lyapunov functional to construct is more general and our results expand the model in [22,29].
When and (, ), system (1) becomes the following BAM NNs with distributed delays and reactiondiffusion terms:
For (23), we get the following result.
Corollary 1Let (A1)(A4) be in force. If there exist (), , , such that
and
where, , , , , , andare Lipschitz constants. Then the equilibrium pointof system (1) is unique and globally exponentially stable.
4 Periodic solutions
In this section, we consider the stability criterion for periodic oscillatory solutions of system (1), in which external input , , and , , are continuously periodic functions with period ω, that is,
By constructing a Poincaré mapping, the existence of a unique ωperiodic solution and its stability are readily established.
Theorem 2Let (A1)(A4) be in force. There exists only oneωperiodic solution of system (1), and all other solutions converge exponentially to it asif there exist constants (), , , (, ) such that
and
whereand, , , , , andare Lipschitz constants in (A3).
Proof For any , we denote the solutions of system (1) through , and , as
and
respectively. Define
Clearly, for any , . Now, we define
Thus, we can obtain from system (1) that
We consider the following Lyapunov functional:
By a minor modification of the proof of Theorem 1, we can easily get
for ,in which is a constant. Now, we can choose a positive integer N such that
Defining a Poincaré mapping by
due to the periodicity of system, we have
Let , then from (26)(29) we can derive that
which shows that is a contraction mapping. Therefore, there exists a unique fixed point , namely, .
Since , then is also a fixed point of . Because of the uniqueness of a fixed point of , then .
Let be the solution of system (1) through , then is also a solution of system (1). Clearly,
This shows that is exactly one ωperiodic solution of system (1), and it is easy to see that all other solutions of system (1) converge exponentially to it as . The proof is completed. □
5 Illustration example
In this section, a numerical example is given to illustrate the effectiveness of the obtained results.
Example 1 Consider the following system on :
where , . , , , , , . , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , . By simple calculation with , and , we have
and
that is, (6) holds.
The simulation results are shown in Figures 18. When , the states surfaces of are shown in Figures 12, while , the states surfaces of are shown in Figures 34. When , the states surfaces of are shown in Figures 56, while , the states surfaces of are shown in Figures 78, which illustrates that the system states in (30) converge to equilibrium solution. Therefore, it follows from Theorem 1 and the simulation study that (30) has one unique equilibrium solution which is globally exponentially stable.
Figure 1. The surface ofwhen.
Figure 2. The surface ofwhen.
Figure 3. The surface ofwhen.
Figure 4. The surface ofwhen.
Figure 5. The surface ofwhen.
Figure 6. The surface ofwhen.
Figure 7. The surface ofwhen.
Figure 8. The surface ofwhen.
Remark 4 Since , the conditions of Corollary 3.2 in [22] and , under the conditions of Example 1, the conditions of Theorem 1 in [29] are not satisfied. However, by (31)(34) and Theorem 1, we can derive that (30) has one unique equilibrium solution which is globally exponentially stable.
6 Conclusions
In this paper, by employing suitable Lyapunov functionals, Young’s inequality and Hölder’s inequality techniques, global exponential stability criteria of the equilibrium point and periodic solutions for RDNNs with mixed time delays and Dirichlet boundary conditions have been derived, respectively. The derived criteria contain and extend some previous NNs in the literature. Hence, our results have an important significance in design as well as in applications of periodic oscillatory NNs with mixed time delays. An example has been given to show the effectiveness of the obtained results.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
WZ designed and performed all the steps of proof in this research and also wrote the paper. JL and MC participated in the design of the study and suggested many good ideas that made this paper possible and helped to draft the first manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors would like to thank the referees for valuable comments and suggestions in improving this paper. This work is partially supported by the National Natural Science Foundation of China under Grant No. 60974139, the Special Research Project in Shaanxi Province Department of Education (2013JK0578) and Doctor Introduced project of Xianyang Normal University under Grant No. 12XSYK008.
References

Kwon, OM, Park, JH, Lee, SM, Cha, EJ: A new augmented LyapunovKrasovskii functional approach to exponential passivity for neural networks with timevarying delays. Appl. Math. Comput.. 217(24), 10231–10238 (2011). Publisher Full Text

Ji, DH, Koo, JH, Won, SC, Lee, SM, Park, JH: Passivitybased control for Hopfield neural networks using convex representation. Appl. Math. Comput.. 217(13), 6168–6175 (2011). Publisher Full Text

Lee, SM, Kwon, OM, Park, JH: A novel delaydependent criterion for delayed neural networks of neutral type. Phys. Lett. A. 374(1718), 1843–1848 (2010). Publisher Full Text

Cao, J, Wang, J: Global exponential stability and periodicity of recurrent neural networks with time delays. IEEE Trans. Circuits Syst. I, Regul. Pap.. 52(5), 920–931 (2005)

Huang, C, Cao, J: Convergence dynamics of stochastic CohenGrossberg neural networks with unbounded distributed delays. IEEE Trans. Neural Netw.. 22(4), 561–572 (2011). PubMed Abstract  Publisher Full Text

Ensari, T, Arik, S: Global stability of a class of neural networks with time varying delay. IEEE Trans. Circuits Syst. II, Express Briefs. 52(3), 126–130 (2005)

Rakkiyappan, R, Balasubramaniam, P: Delaydependent asymptotic stability for stochastic delayed recurrent neural networks with time varying delays. Appl. Math. Comput.. 198(2), 526–533 (2008). Publisher Full Text

Kosko, B: Bidirectional associative memories. IEEE Trans. Syst. Man Cybern.. 18(1), 49–60 (1988). Publisher Full Text

Park, JH, Kwon, OM: Delaydependent stability criterion for bidirectional associative memory neural networks with interval timevarying delays. Mod. Phys. Lett. B. 23(1), 35–46 (2009). Publisher Full Text

Park, JH, Park, CH, Kwon, OM, Lee, SM: New stability criterion for bidirectional associative memory neural networks of neutraltype. Appl. Math. Comput.. 199(2), 716–722 (2008). Publisher Full Text

Park, JH, Kwon, OM: On improved delaydependent criterion for global stability of bidirectional associative memory neural networks with timevarying delays. Appl. Math. Comput.. 199(2), 435–446 (2008). Publisher Full Text

Cao, J, Wang, L: Exponential stability and periodic oscillatory solution in BAM networks with delays. IEEE Trans. Neural Netw.. 13(2), 457–463 (2002). PubMed Abstract  Publisher Full Text

Park, J, Lee, SM, Kwon, OM: On exponential stability of bidirectional associative memory neural networks with timevarying delays. Chaos Solitons Fractals. 39(3), 1083–1091 (2009). Publisher Full Text

Wu, R: Exponential convergence of BAM neural networks with timevarying coefficients and distributed delays. Nonlinear Anal., Real World Appl.. 11(1), 562–573 (2010). Publisher Full Text

Liu, X, Martin, R, Wu, M: Global exponential stability of bidirectional associative memory neural networks with time delays. IEEE Trans. Neural Netw.. 19(2), 397–407 (2008). PubMed Abstract  Publisher Full Text

Zhang, W, Li, J: Global exponential synchronization of delayed BAM neural networks with reactiondiffusion terms and the Neumann boundary conditions. Bound. Value Probl.. 2012, (2012) Article ID 2. doi:10.1186/1687277020122

Zhang, W, Li, J, Shi, N: Stability analysis for stochastic Markovian jump reactiondiffusion neural networks with partially known transition probabilities and mixed time delays. Discrete Dyn. Nat. Soc.. 2012, (2012) Article ID 524187. doi:10.1155/2012/524187

Song, Q, Zhao, Z, Li, YM: Global exponential stability of BAM neural networks with distributed delays and reactiondiffusion terms. Phys. Lett. A. 335(23), 213–225 (2005). Publisher Full Text

Zhang, W, Li, J: Global exponential stability of reactiondiffusion neural networks with discrete and distributed timevarying delays. Chin. Phys. B. 20(3), (2011) Article ID 030701

Pan, J, Zhan, Y: On periodic solutions to a class of nonautonomously delayed reactiondiffusion neural networks. Commun. Nonlinear Sci. Numer. Simul.. 16(1), 414–422 (2011). Publisher Full Text

Song, Q, Cao, J: Global exponential stability and existence of periodic colutions in BAM with delays and reactiondiffusion terms. Chaos Solitons Fractals. 23(2), 421–430 (2005). Publisher Full Text

Cui, B, Lou, X: Global asymptotic stability of BAM neural networks with distributed delays and reactiondiffusion terms. Chaos Solitons Fractals. 27(5), 1347–1354 (2006). Publisher Full Text

Zhao, H, Wang, G: Existence of periodic oscillatory solution of reactiondiffusion neural networks with delays. Phys. Lett. A. 343(5), 372–383 (2005). Publisher Full Text

Song, Q, Cao, J, Zhao, Z: Periodic solutions and its exponential stability of reactiondiffusion recurrent neural networks with continuously distributed delays. Nonlinear Anal., Real World Appl.. 7(1), 65–80 (2006). Publisher Full Text

Wang, Z, Zhang, H: Global asymptotic stability of reactiondiffusion CohenGrossberg neural network with continuously distributed delays. IEEE Trans. Neural Netw.. 21(1), 39–49 (2010). PubMed Abstract  Publisher Full Text

Zhang, X, Wu, S, Li, K: Delaydependent exponential stability for impulsive CohenGrossberg neural networks with timevarying delays and reactiondiffusion terms. Commun. Nonlinear Sci. Numer. Simul.. 16(3), 1524–1532 (2011). Publisher Full Text

Wang, L, Zhang, R, Wang, Y: Global exponential stability of reactiondiffusion cellular neural networks with Stype distributed time delays. Nonlinear Anal., Real World Appl.. 10(2), 1101–1113 (2009). Publisher Full Text

Zhu, Q, Li, X, Yang, X: Exponential stability for stochastic reactiondiffusion BAM neural networks with timevarying and distributed delays. Appl. Math. Comput.. 217(13), 6078–6091 (2011). Publisher Full Text

Lou, X, Cui, B, Wu, W: On global exponential stability and existence of periodic solutions for BAM neural networks with distributed delays and reactiondiffusion terms. Chaos Solitons Fractals. 36(4), 1044–1054 (2008). Publisher Full Text

Zhang, W, Li, J, Chen, M: Dynamical behaviors of impulsive stochastic reactiondiffusion neural networks with mixed time delays. Abstr. Appl. Anal.. 2012, (2012) Article ID 236562. doi:10.1155/2012/236562

Wang, Z, Zhang, H, Li, P: An LMI approach to stability analysis of reactiondiffusion CohenGrossberg neural networks concerning Dirichlet boundary conditions and distributed delays. IEEE Trans. Syst. Man Cybern., Part B, Cybern.. 40(6), 1596–1606 (2010)

Lu, J: Robust global exponential stability for interval reactiondiffusion Hopfield neural networks with distributed delays. IEEE Trans. Circuits Syst. II, Express Briefs. 54(12), 1115–1119 (2007)

Lu, J, Lu, L: Global exponential stability and periodicity of reactiondiffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions. Chaos Solitons Fractals. 39(4), 1538–1549 (2009). Publisher Full Text

Wang, J, Lu, J: Global exponential stability of fuzzy cellular neural networks with delays and reactiondiffusion terms. Chaos Solitons Fractals. 38(3), 878–885 (2008). Publisher Full Text