Global exponential synchronization of delayed BAM neural networks with reaction-diffusion terms and the Neumann boundary conditions

In this article, a delay-differential equation modeling a bidirectional associative memory (BAM) neural networks (NNs) with reaction-diffusion terms is investigated. A feedback control law is derived to achieve the state global exponential synchronization of two identical BAM NNs with reaction-diffusion terms by constructing a suitable Lyapunov functional, using the drive-response approach and some inequality technique. A novel global exponential synchronization criterion is given in terms of inequalities, which can be checked easily. A numerical example is provided to demonstrate the effectiveness of the proposed results.

Aihara et al. [1] firstly proposed chaotic neural network (NN) models to simulate the chaotic behavior of biological neurons. Consequently, chaotic NNs have drawn considerable attention and have successfully been applied in combinational optimization, secure communication, information science, and so on [2-4]. Since NNs related to bidirectional associative memory (BAM) have been proposed by Kosko [5], the BAM NNs have been one of the most interesting research topics and extensively studied because of its potential applications in pattern recognition, etc. Hence, the study of the stability and periodic oscillatory solution of BAM with delays has raised considerable interest in recent years, see for example [6-12] and the references cited therein.

Strictly speaking, diffusion effects cannot be avoided in the NNs when electrons are moving in asymmetric electromagnetic fields. Therefore, we must consider that the activations vary in space as well as in time. In [13-27], the authors have considered various dynamical behaviors such as the stability, periodic oscillation, and synchronization of NNs with diffusion terms, which are expressed by partial differential equations. For instance, the authors of [16] discuss the impulsive control and synchronization for a class of delayed reaction-diffusion NNs with the Dirichlet boundary conditions in terms of p-norm. In [25], the synchronization scheme is discussed for a class of delayed NNs with reaction-diffusion terms. In [26], an adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of NNs with reaction-diffusion terms. Meanwhile, although the models of delayed feedback with discrete delays are good approximation in simple circuits consisting of a small number of cells, NNs usually have a spatial extent due to the presence of a multitude of parallel pathways with a variety of axon sizes and lengths. Thus, there is a distribution of conduction velocities along these pathways and a distribution of propagation delays. Therefore, the models with discrete and continuously distributed delays are more appropriate.

To the best of the authors' knowledge, global exponential synchronization is seldom reported for the class of delayed BAM NNs with reaction-diffusion terms. In the theory of partial differential equations, Poincaré integral inequality is often utilized in the deduction of diffusion operator [28]. In this article, the problem of global exponential synchronization is investigated for the class of BAM NNs with time-varying and distributed delays and reaction-diffusion terms by using Poincaré integral inequality, Young inequality technique, and Lyapunov method, which are very important in theories and applications and also are a very challenging problem. Several sufficient conditions are in the form of a few algebraic inequalities, which are very convenient to verify.

In this article, a class of delayed BAM NNs with reaction-diffusion terms is described as follows

where x = (x₁, x₂ ,..,x_l)^T ∈ Ω ⊂ ℝ^l, Ω is a compact set with smooth boundary ∂Ω and mesΩ > 0 in space ℝ^l; u = (u₁,u₂,...,u_m)^T ∈ ℝ^m, (v₁,v₂,...,v_n)^T ∈ ℝⁿ, u_i(t,x) and v_j(t,x) and represent the states of the ith neurons and the jth neurons at time t and in space x, respectively. , and are known constants denoting the synaptic connection strengths between the neurons, respectively; f_i and g_i denote the activation functions of the neurons and the signal propagation functions, respectively; I_i and J_i denote the external inputs on the ith and jth neurons, respectively; p_i and q_j are differentiable real functions with positive derivatives defining the neuron charging time, respectively; τ_ij(t) and θ_ji(t) represent continuous time-varying discrete delays, respectively; D_ik ≥ 0 and stand for the transmission diffusion coefficient along the ith and jth neurons, respectively. i = 1, 2, ..., m, k = 1, 2, l and j = 1, 2,..., n.

System (1) is supplemented with the following boundary conditions and initial values

for any i = 1,2,..., m and j = 1,2,..., n where is the outer normal vector of ∂Ω, are bounded and continuous, where It is the Banach space of continuous functions which map into ℝ^m+n with the topology of uniform converge for the norm

Throughout this article, we assume that the following conditions are made.

(A1) The functions τ_ij(t), θ_ji(t) are piecewise-continuous of class C¹ on the closure of each continuity subinterval and satisfy

with some constants τ_ij ≥ 0, θ_ji ≥ 0, τ > 0, θ > 0, for all t ≥ 0.

(A2) The functions p_i (·)and q_j(·) are piecewise-continuous of class C¹ on the closure of each continuity subinterval and satisfy

(A3) The activation functions are bounded and Lipschitz continuous, i.e., there exist positive constants and such that for all η₁, η₂ ∈ ℝ

(A4) The delay kernels (i = 1, 2,...,m, j = 1, 2,...,n) are real-valued non-negative continuous functions that satisfy the following conditions

(i)

(ii)

(iii)There exist a positive μ such that

We consider system (1) as the drive system. The response system is described by the following equations

where σ_i (t,x) and ϑ_j(t,x) denote the external control inputs that will be appropriately designed for a certain control objective. We denote , and ϑ(t,x) = (ϑ₁(t,x),..., ϑ_n(t,x))^T.

The boundary and initial conditions of system (4) are

and

where .

Definition 1. Drive-response systems (1) and (4) are said to be globally exponentially synchronized, if there are control inputs σ(t,x), ϑ(t,x), and r ≥ 2, further there exist constants α > 0 and β ≥ 1 such that

, for all t ≥ 0,

in which , , and (u(t,x), v(t,x)) and are the solutions of drive-response systems (1) and (4) satisfying boundary conditions and initial conditions (2), (3) and (5), (6), respectively.

Lemma 1. [21] (Poincaré integral inequality). Let Ω be a bounded domain of ℝ^m with a smooth boundary ∂Ω of class C² by Ω. u(x) is a real-valued function belonging to and Then

which λ₁ is the lowest positive eigenvalue of the Neumann boundary problem

From the definition of synchronization, we can define the synchronization error signal , e(t,x) = (e₁(t,x),...,e_m(t,x))^T, and ω(t,x) = (ω₁(t,x),..., ω_n(t,x))^T . Thus, error dynamics between systems (1) and (4) can be expressed by

where , .

The control inputs strategy with state feedback are designed as follows:

that is,

where and are the controller gain matrices.

The global exponential synchronization of systems (1) and (4) can be solved if the controller matrices μ and ρ are suitably designed. We have the following result.

Theorem 1. Under the assumptions (A1)-(A4), drive-response systems (1) and (4) are in global exponential synchronization, if there exist w_i > 0(i = 1,2,..., n+m), r ≥ 2, γ_ij > 0, β_ji > 0 such that the controller gain matrices μ and ρ in (9) satisfy

and

in which i = 1, 2, ..., m, j = 1, 2,..., n, and are Lipschitz constants, λ₁ is the lowest positive eigenvalue of problem (7).

Proof. If (10) holds, we can always choose a positive number δ > 0 (may be very small) such that

and

where i = 1, 2,..., m, j = 1, 2,..., n.

Let us consider functions

and

where i = 1, 2, ..., m, j = 1, 2, ..., n.

From (12) and (A4), we derive

F_i(0) < -δ < 0, G_j(0) < -δ < 0; and are continuous for Moreover, as and as , thus there exist constants ε_i,ν_j ∈ [0, +∞) such that

and

By using obviously, we get

and

Multiplying both sides of the first equation of (8) by e_i (t,x) and integrating over Ω yields

It is easy to calculate by the Neumann boundary conditions (2) that

Moreover, from Lemma 1, we can derive

From (13)-(17), (A2), and (A3), we obtain that

Multiplying both sides of the second equation of (8) by ω_j (t,x), similarly, we also have

Consider the following Lyapunov functional

Its upper Dini-derivative along the solution to system (8) can be calculated as

From (21) and Young inequality, we can conclude

From (10), we can conclude

Since

Noting that

Let

Clearly, β ≥ 1.

It follows that

for any t ≥ 0 where β ≥ 1 is a constant. This implies that drive-response systems (1) and (4) are globally exponentially synchronized. This completes the proof of Theorem 1.

Remark 1. In Theorem 1, the Poincaré integral inequality is used firstly. This is a very important step. Thus, the derived sufficient condition includes diffusion terms. We note that, in the proof in the previous articles [24-26], a negative integral term with gradient is left out in their deduction. This leads to those criteria that are irrelevant to the diffusion term. Therefore, Theorem 1 is essentially new and more effectiveness than those obtained.

Remark 2. It is noted that we construct a novel Lyapunov functional here as defined in (20) since the considered model contains time-varying and distributed delays and reaction-diffusion terms. We can see that the results and research method obtained in this article can also be extended to many other types of NNs with reaction-diffusion terms, e.g., the cellular NNs, cohen-grossberg NNs, etc.

Remark 3. In our result, the effects of the reaction-diffusion terms on the synchronization are considered. Furthermore, we note a very interesting fact, that is, as long as diffusion coefficients in the system are large enough, then condition (10) can always satisfy. This shows that a large enough diffusion coefficient may always make the system globally exponentially synchronous.

Some famous NN models are a special case of model (1). In system (1), ignoring the role of reaction-diffusion, then system (1) will degenerate into the following delayed BAM NNs

and the corresponding response system (4) will become the following form

Define the synchronization error signal , then the error dynamics between systems (27) and (28) can be expressed by

We consider the following control inputs strategy

As a consequence of Theorem 1, we have the following result:

Corollary 1. Under the assumptions (A1)-(A4), drive-response systems (27) and (28) are in global exponential synchronization, if there exist w_i > 0 (i = 1, 2,...,n+m), r ≥ 2, γ_ij > 0, β_ji > 0 such that the controller gain matrices μ and ρ in (9) satisfy

and

in which i = 1, 2,..., m, j = 1, 2,..., n, and are Lipschitz constants.

To illustrate the effectiveness of our criterion, we give the following example.

Example 1. Consider the following system on

and

where

By simple calculation with w₁ = w₂ = w₃ = w₄ = 1, β₁₁ = β₁₂ = β₂₁ = β₂₂ = 1,

and γ₁₁ = γ₁₂ = γ₂₁ = γ₂₂ = 1, we get

and

Hence, it follows from Theorem 1 that (32) and (33) are globally exponentially synchronized.

In this article, global exponential synchronization has been considered for a class of BAM NNs with time-varying and distributed delays and reaction-diffusion terms. We have established a new sufficient condition which includes the diffusion coefficients by constructing the suitable Lyapunov functional, introducing many real parameters and applying inequality techniques. From condition (10) in Theorem 1, we see that diffusion coefficients directly affect the synchronization behavior of the delayed BAM NNs with reaction-diffusion terms. In comparison with previous literature, diffusion effects are taken into account in our models. A numerical example has been given to show the effectiveness of the obtained results.

The authors declare that they have no competing interests.

WZ designed and performed all the steps of proof in this research and also wrote the paper. JL participated in the design of the study and suggest many good ideas that made this paper possible and helped to draft the first manuscript. All authors read and approved the final manuscript.

This study was partially supported by the National Natural Science Foundation of China under Grant No. 60974139 and partially supported by the Fundamental Research Funds for the Central Universities under Grant No. 72103676, the Natural Science Foundation of Shannxi Province, China under Grant No. 2010JQ1013, and the Special research projects in Shannxi Province Department of Education under Grant No. 2010JK896.

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