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RESEARCH Open Access
Global exponential synchronization of delayedBAM neural networks
with reaction-diffusionterms and the Neumann boundary
conditionsWeiYuan Zhang1,2* and JunMin Li1
* Correspondence: [email protected] of Science,
XidianUniversity, Shaan Xi Xi’an 710071,P.R. ChinaFull list of
author information isavailable at the end of the article
Abstract
In this article, a delay-differential equation modeling a
bidirectional associativememory (BAM) neural networks (NNs) with
reaction-diffusion terms is investigated. Afeedback control law is
derived to achieve the state global exponentialsynchronization of
two identical BAM NNs with reaction-diffusion terms byconstructing
a suitable Lyapunov functional, using the drive-response approach
andsome inequality technique. A novel global exponential
synchronization criterion isgiven in terms of inequalities, which
can be checked easily. A numerical example isprovided to
demonstrate the effectiveness of the proposed results.
Keywords: neural networks, reaction-diffusion, delays, global
exponential synchroni-zation, Lyapunov functional
1. IntroductionAihara et al. [1] firstly proposed chaotic neural
network (NN) models to simulate the
chaotic behavior of biological neurons. Consequently, chaotic
NNs have drawn consid-
erable 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 synchroniza-
tion 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 con-
troller is derived to achieve the exponential synchronization of
the drive-response
structure of NNs with reaction-diffusion terms. Meanwhile,
although the models of
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© 2012 Zhang and Li; licensee Springer. This is an Open Access
article distributed under the terms of the Creative
CommonsAttribution License
(http://creativecommons.org/licenses/by/2.0), which permits
unrestricted use, distribution, and reproduction inany medium,
provided the original work is properly cited.
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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 pre-
sence 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 distri-
bution 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 the-
ory 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 dis-
tributed delays and reaction-diffusion terms by using Poincaré
integral inequality,
Young inequality technique, and Lyapunov method, which are very
important in the-
ories and applications and also are a very challenging problem.
Several sufficient condi-
tions are in the form of a few algebraic inequalities, which are
very convenient to
verify.
2. Model description and preliminariesIn this article, a class
of delayed BAM NNs with reaction-diffusion terms is described
as follows
∂ui∂t
=l∑
k=1
∂
∂xk
(Dik
∂ui∂xk
)− pi (ui (t, x))
+n∑j=1
bjifj(vj (t, x)
)+
n∑j=1
b̃jifj(vj
(t − θji (t) , x
))+
n∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(vj (s, x)
)ds + Ii (t) ,
∂vj∂t
=l∑
k=1
∂
∂xk
(D∗jk
∂vj∂xk
)− qj
(vj (t, x)
)+
m∑i=1
dijgi (ui (t, x))
+m∑i=1
d̃ijgi(ui
(t − τij (t) , x
))+
m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi (ui (s, x)) ds + Jj (t) ,
(1)
where x = (x1, x2 ,..,xl)T Î Ω ⊂ ℝl, Ω is a compact set with
smooth boundary ∂Ω and
mesΩ > 0 in space ℝl; u = (u1,u2,...,um)T Î ℝm,
(v1,v2,...,vn)
T Î ℝn, ui(t,x) and vj(t,x) andrepresent the states of the ith
neurons and the jth neurons at time t and in space x,
respectively. bji, b̃ji, b̄ji,dij, d̄ij, and d̃ij are known
constants denoting the synaptic connec-
tion strengths between the neurons, respectively; fi and gi
denote the activation func-
tions of the neurons and the signal propagation functions,
respectively; Ii and Ji denote
the external inputs on the ith and jth neurons, respectively; pi
and qj are differentiable
real functions with positive derivatives defining the neuron
charging time, respectively;
τij(t) and θji(t) represent continuous time-varying discrete
delays, respectively; Dik ≥ 0
and D∗jk ≥ 0 stand for the transmission diffusion coefficient
along the ith and jth neu-rons, 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
∂ui∂n̄
:=(
∂ui∂x1
,∂ui∂x2
, ...,∂ui∂xl
)T= 0,
∂vj∂n̄
:=(
∂vj∂x1
,∂vj∂x2
, ...,∂vj∂xl
)T= 0, t ≥ 0, x ∈ ∂�, (2)
ui (s, x) = ϕui (s, x) , vj (s, x) = ϕvj (s, x) , (s, x) ∈ (−∞,
0] × �. (3)
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for any i = 1,2,..., m and j = 1,2,..., n where n̄ is the outer
normal vector of
∂Ω,ϕ =(
ϕuϕv
)= (ϕu1, ...,ϕum,ϕv1, ...,ϕvn)T ∈ C are bounded and continuous,
where
C ={ϕ|ϕ =
(ϕuϕv
),ϕ :
((−∞, 0] × Rm(−∞, 0] × Rn
)→ Rm+n
}. It is the Banach space of continuous
functions which map
((−∞, 0](−∞, 0]
)into ℝm+n with the topology of uniform converge for the
norm
‖ϕ‖ =∥∥∥∥(
ϕuϕv
)∥∥∥∥ = sup−∞≤s≤0[∫
�
m∑i=1
|ϕui|rdx]+ sup
−∞≤s≤0
⎡⎣∫
�
n∑j=1
∣∣ϕvj∣∣rdx⎤⎦ , r ≥ 2.
Throughout this article, we assume that the following conditions
are made.
(A1) The functions τij(t), θji(t) are piecewise-continuous of
class C1 on the closure of
each continuity subinterval and satisfy
0 ≤ τij (t) ≤ τij, 0 ≤ θji (t) ≤ θji, τ̇ij (t) ≤ μτ < 1, θ̇ji
(t) ≤ μθ < 1,τ = max
1≤i≤m,1≤j≤n{τij
}, θ = max
1≤i≤m,1≤j≤n{θji
},
with some constants τij ≥ 0, θji ≥ 0, τ > 0, θ >0, for all
t ≥ 0.
(A2) The functions pi (·)and qj(·) are piecewise-continuous of
class C1 on the closure
of each continuity subinterval and satisfy
ai = infζ∈R
pi ′ (ζ ) > 0, pi (0) = 0,
cj = infζ∈R
qj′ (ζ ) > 0, qj (0) = 0.
(A3) The activation functions are bounded and Lipschitz
continuous, i.e., there exist
positive constants Lfj and Lgi such that for all h1, h2 Î ℝ∣∣fj
(η1) − fj (η2)∣∣ ≤ Lfj |η1 − η2| , ∣∣gi (η1) − gi (η2)∣∣ ≤ Lgi |η1
− η2| .
(A4) The delay kernels Kji (s) , K̄ij (s) : [0,∞) → [0,∞) ,(i =
1, 2,...,m, j = 1, 2,...,n) arereal-valued non-negative continuous
functions that satisfy the following conditions
(i)∫ +∞0 Kji (s) ds = 1,
∫ +∞0 K̄ji (s) ds = 1,
(ii)∫ +∞0 sKji (s) ds < ∞,
∫ +∞0 sK̄ij (s) ds < ∞,
(iii)There exist a positive μ such that∫ +∞0
seμsKji (s) ds < ∞,∫ +∞0
seμsK̄ij (s) ds < ∞.
We consider system (1) as the drive system. The response system
is described by the
following equations
∂ũi (t, x)∂t
=l∑
k=1
∂
∂xk
(Dik
∂ũi (t, x)∂xk
)− pi
(ũi (t, x)
)+
n∑j=1
bjifj(ṽj (t, x)
)
+n∑j=1
b̃jifj(ṽj
(t − θji (t) , x
))+
n∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(ṽj (s, x)
)ds + Ii (t) + σi (t, x) ,
∂ ṽj (t, x)∂t
=l∑
k=1
∂
∂xk
(D∗jk
∂ ṽj (t, x)∂xk
)− qj
(ṽj (t, x)
)+
m∑i=1
dijgi(ũi (t, x)
)
+m∑i=1
d̃ijgi(ũi
(t − τij (t) , x
))+
m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi(ũi (s, x)
)ds + Jj (t) + ϑj (t, x) ,
(4)
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where si (t,x) and ϑj(t,x) denote the external control inputs
that will be appropriately
designed for a certain control objective. We denote ũ (t, x)
=(ũ1 (t, x) , ..., ũm (t, x)
)T,ṽ (t, x) =
(ṽ1 (t, x) , ..., ṽn (t, x)
)T , σ (t, x) = (σ1 (t, x) , ..., σm (t, x))T and ϑ(t,x) =
(ϑ1(t,x),...,ϑn(t,x))
T.
The boundary and initial conditions of system (4) are
∂ũi∂n̄
:=(
∂ũi∂x1
,∂ũi∂x2
, ...,∂ũi∂xl
)T= 0,
∂ ṽj∂n̄
:=(
∂ ṽj∂x1
,∂ ṽj∂x2
, ...,∂ ṽj∂xl
)T= 0 t ≥ 0, x ∈ ∂�, (5)
and
ũi (s, x) = ψui (s, x) , ṽj (s, x) = ψvj (s, x) , (s, x) ∈
(−∞, 0] × �, (6)
where ψ =(
ψũψṽ
)=
(ψũ1, ...,ψũm,ψṽ1, ...,ψṽn
)T ∈ C.Definition 1. Drive-response systems (1) and (4) are said
to be globally exponentially
synchronized, if there are control inputs s(t,x), ϑ(t,x), and r
≥ 2, further there existconstants a > 0 and b ≥ 1 such that∥∥u
(t, x) − ũ (t, x)∥∥ + ∥∥v (t, x) − ṽ (t, x)∥∥
≤ βe−2αt (‖ϕu (s, x) − ψũ (s, x)‖ + ‖ϕv (s, x) − ψṽ (s, x)‖),
for all t ≥ 0,
in which∥∥u (t, x) − ũ (t, x)∥∥ = ∫
�
m∑i=1
∣∣ui (t, x) − ũi (t, x)∣∣rdx,∥∥v (t, x) − ṽ (t, x)∥∥ = ∫
�
n∑j=1
∣∣vj (t, x) − ṽj (t, x)∣∣rdx, r ≥ 2, and (u(t,x), v(t,x))
and(ũ (t, x) , ṽ (t, x)
)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 ℝm
with a smooth boundary ∂Ω of class C2 by Ω. u(x) is a
real-valued function belonging
to H10 (�) and∂u (x)
∂n̄|∂� = 0. Then
∫�
|u (x)|2dx ≤ 1λ1
∫�
|∇u (x)|2dx,
which l1 is the lowest positive eigenvalue of the Neumann
boundary problem⎧⎨⎩
−�ϕ (x) = λ1ϕ (x) , x ∈ �,∂u (x)
∂n̄|∂� = 0, x ∈ ∂�. (7)
3. Main resultsFrom the definition of synchronization, we can
define the synchronization error signal
ei (t, x) = ui (t, x) − ũi (t, x) ,ωj (t, x) = vj (t, x) − ṽj
(t, x), e(t,x) = (e1(t,x),...,em(t,x))T, andω(t,x) = (ω1(t,x),...,
ωn(t,x))
T . Thus, error dynamics between systems (1) and (4) can be
expressed by
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∂ei (t, x)∂t
=l∑
k=1
∂
∂xk
(Dik
∂ei (t, x)∂xk
)− p̃i (ei (t, x)) +
n∑j=1
bjif̃j(ωj (t, x)
)
+n∑j=1
b̃ji f̃j(ωj
(t − θji (t) , x
))+
n∑j=1
b̄ji
∫ t−∞
kji (t − s) f̃j(ωj (s, x)
)ds − σi (t, x) ,
∂ωj (t, x)∂t
=l∑
k=1
∂
∂xk
(D∗jk
∂ωj (t, x)∂xk
)− q̃j
(ωj (t, x)
)+
m∑i=1
(dijg̃i (ei (t, x))
)
+m∑i=1
(d̃ijg̃i
(ei
(t − τij (t) , x
)))+
m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) g̃i (ei (s, x)) ds − ϑj (t, x) ,
(8)
where f̃j(ωj (t, x)
)= fj
(vj (t, x)
) − fj (ṽj (t, x)) , g̃i (ei (t, x)) = gi (ui (t, x)) − gi (ũi
(t, x)),p̃i (ei (t, x)) = pi (ui (t, x)) − pi
(ũi (t, x)
), q̃j
(ωj (t, x)
)= qj
(vj (t, x)
) − qj (ṽj (t, x)).The control inputs strategy with state
feedback are designed as follows:
σi (t, x) =m∑k=1
μikek (t, x) ,ϑj (t, x) =n∑
k=1
ρjkωk (t, x) , i = 1, 2, ...,m, j = 1, 2, ...,n.
that is,
σ (t, x) = μe (t, x) ,ϑ (t, x) = ρω (t, x) , (9)
where μ = (μik)m×m and ρ =(ρjk
)n×n are the controller gain matrices.
The global exponential synchronization of systems (1) and (4)
can be solved if the
controller matrices μ and r 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 wi
> 0(i = 1,2,..., n+m), r ≥ 2,
gij > 0, bji > 0 such that the controller gain matrices μ
and r in (9) satisfy
wi
⎛⎜⎝−rnar−1i Diλ1 − rnari − rnμiiar−1i + 2 (r − 1)
n∑j=1
ari + (r − 1)n∑j=1
ariβ−
rr − 1
ji + (r − 1)m∑
k=1,i�=kari
⎞⎟⎠
+n∑j=1
wm+jmr(∣∣dij∣∣r(Lgi )r + ∣∣∣d̃ij∣∣∣r eτ1 − μτ
(Lgi
)r+∣∣∣d̄ij∣∣∣rγ rij(Lgi )r) + nr
m∑k=1,i�=k
|μki|rwk < 0
and
wm+j
⎛⎜⎝−rmcr−1j D∗j λ1 − rmcrj − rmρjjcr−1j + 2 (r − 1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj + (r − 1)
m∑i=1
crj γ−
rr − 1
ij
⎞⎟⎠
+m∑i=1
winr(∣∣bji∣∣r(Lfj )r + ∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj)r) +mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m < 0,(10)
in which i = 1, 2, ..., m, j = 1, 2,..., n, Lfj and Lgi are
Lipschitz constants,
Di = min1≤k≤l
Dik,D∗j = min1≤k≤l
D∗jk,l1 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
wi
⎛⎜⎝−rnar−1i Diλ1 − rnari − rnμiiar−1i + 2 (r − 1)
n∑j=1
ari + (r − 1)n∑j=1
ariβ−
rr − 1
ji + (r − 1)m∑
k=1,i�=kari
⎞⎟⎠
+n∑j=1
wm+jmr(∣∣dij∣∣r(Lgi )r + ∣∣∣d̃ij∣∣∣r eτ1 − μτ
(Lgi
)r+∣∣∣d̄ij∣∣∣rγ rij(Lgi )r) + nr
m∑k=1,i�=k
|μki|rwk + δ < 0
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and
wm+j
⎛⎜⎝−rmcr−1j D∗j λ1 − rmcrj − rmρjjcr−1j + 2 (r − 1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj + (r − 1)
m∑i=1
crj γ−
rr − 1
ij
⎞⎟⎠
+m∑i=1
winr
⎛⎝∣∣bji∣∣r(Lfj )r + ∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj )r) +mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m + δ < 0,(11)
where i = 1, 2,..., m, j = 1, 2,..., n.
Let us consider functions
Fi(x∗i
)= wi
[−rnar−1i Diλ1 − rnari − rnμiiar−1i + 2 (r − 1)n∑j=1
ari + (r − 1)m∑
k=1,i�=kari
+ (r − 1)n∑j=1
ariβ−
rr − 1
ji
∫ +∞0
kji (s) ds + 2x∗i nar−1i
⎤⎥⎦ + n∑
j=1
wm+jmr(∣∣dij∣∣r(Lgi )r
+∣∣∣d̃ij∣∣∣r eτ1 − μτ
(Lgi
)r+∣∣∣d̄ij∣∣∣rγ rij(Lgi )r
∫ +∞0
e2x∗i sk̄ij (s) ds
)+ nr
m∑k=1,i�=k
|μki|rwk
and
Gj(y∗j
)= wm+j
[−rmcr−1j D∗j λ1 − rmcrj − rmρjjcr−1j + 2 (r − 1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj
+ (r − 1)m∑i=1
crjγ−
r
r − 1ij
∫ +∞0
k̄ij (s) ds + 2y∗j mcr−1j
⎤⎥⎦ + m∑
i=1
winr(∣∣bji∣∣r(Lfj )r
+∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj )r
∫ +∞0
e2y∗j skji (s) ds
)+mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m,
(12)
where x∗i , y∗j ∈ [0, +∞) ,i = 1, 2, ..., m, j = 1, 2, ...,
n.
From (12) and (A4), we derive
Fi(0) < -δ < 0, Gj(0) < -δ
-
and
Gj(νj)= wm+j
(−rmcr−1j D∗j λ1 − rmcrj − rmρjjcr−1j + 2 (r − 1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj
+ (r − 1)m∑i=1
crjγ−
rr − 1
ij
∫ +∞0
k̄ij (s) ds +2νjmcr−1j
)+
m∑i=1
winr(∣∣bji∣∣r(Lfj )r
+∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj )r
∫ +∞0
e2νj skji (s) ds)+mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m = 0.(13)
By using α = min1≤i≤m,1≤j≤n
{εi, νj
}, obviously, we get
Fi (α) = wi(−rnar−1i Diλ1 − rnari − rnμiiar−1i + 2 (r − 1)
n∑j=1
ari + (r − 1)m∑
k=1,i�=kari
+ (r − 1)n∑j=1
ariβ−
rr − 1
ji
∫ +∞0
kji (s) ds + 2αnar−1i ) +
n∑j=1
wm+jmr(∣∣dij∣∣r(Lgi )r
+∣∣∣d̃ij∣∣∣r eτ1 − μτ
(Lgi
)r+∣∣∣d̄ij∣∣∣rγ rij(Lgi )r
∫ +∞0
e2αsk̄ij (s) ds)+ nr
m∑k=1,i�=k
|μki|rwk ≤ 0
and
Gj (α) = wm+j(−rmcr−1j D∗j λ1 − rmcrj − rmρjjcr−1j + 2 (r −
1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj
+ (r − 1)m∑i=1
crj γ−
rr − 1
ij
∫ +∞0
k̄ij (s) ds +2αmcr−1j
)+
m∑i=1
winr(∣∣bji∣∣r(Lfj )r
+∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj)r
∫ +∞0
e2αskji (s) ds)+mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m ≤ 0.(14)
Multiplying both sides of the first equation of (8) by ei (t,x)
and integrating over Ω yields
12ddt
∫�
ei (t, x)2dx =∫
�
l∑k=1
ei (t, x)∂
∂xk
(Dik
∂ei (t, x)∂xk
)dx − p′ i (ξi)
∫�
ei(t, x)2dx
+∫
�
n∑j=1
bjiei (t, x) f̃j(ωj (t, x)
)dx +
n∑j=1
∫�
b̃jiei (t, x) f̃j(ωj
(t − θji (t) , x
))dx
+n∑j=1
∫�
b̄jiei (t, x)∫ t
−∞kji (t − s) f̃j
(ωj (s, x)
)dsdx −
∫�
m∑k=1
ei (t, x) μikek (t, x) dx.
(15)
It is easy to calculate by the Neumann boundary conditions (2)
that
∫�
l∑k=1
ei (t, x)∂
∂xk
(Dik
∂ei (t, x)∂xk
)dx =
∫�
l∑k=1
ei (t, x)∇(Dik
∂ei (t, x)∂xk
)dx
=∫
∂�
l∑k=1
ei (t, x)Dik∂ei (t, x)
∂xkdx −
∫�
l∑k=1
Dik
(∂ei (t, x)
∂xk
)2dx = −
l∑k=1
∫�
Dik
(∂ei (t, x)
∂xk
)2dx
(16)
Moreover, from Lemma 1, we can derive
−l∑
k=1
∫�
Dik
(∂ei (t, x)
∂xk
)2dx ≤ −
l∑k=1
∫�
Di
(∂ei (t, x)
∂xk
)2dx ≤ −Diλ1 ‖ei (t, x)‖22 . (17)
Zhang and Li Boundary Value Problems 2012,
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-
From (13)-(17), (A2), and (A3), we obtain that
ddt
∫�
|ei (t, x)|2dx ≤ −2Diλ1∫
�
|ei (t, x)|2dx − 2ai∫
�
|ei (t, x)|2dx
+2∫
�
n∑j=1
∣∣bji∣∣ |ei (t, x)| Lfj ∣∣ωj (t, x)∣∣dx + 2n∑j=1
∫�
∣∣∣b̃ji∣∣∣ |ei (t, x)| ∣∣∣f̃j (ωj (t − θji (t) , x))∣∣∣ dx+2
n∑j=1
∫�
∣∣∣b̄ji∣∣∣∫ t
−∞kji (t − s) |ei (t, x)|
∣∣∣f̃j (ωj (s, x))∣∣∣ dsdx − 2∫
�
m∑k=1
|ei (t, x)|μik |ek (t, x)| dx.
(18)
Multiplying both sides of the second equation of (8) by ωj
(t,x), similarly, we also have
ddt
∫�
∣∣ωj (t, x)∣∣2dx ≤ −2D∗j λ1∫�
∣∣ωj (t, x)∣∣2dx − 2cj∫
�
∣∣ωj (t, x)∣∣2dx+2
∫�
m∑i=1
∣∣dij∣∣ Lgi |ei (t, x)| ∣∣ωj (t, x)∣∣dx + 2m∑i=1
∫�
∣∣∣d̃ij∣∣∣ ∣∣g̃i (ei (t − τij (t) , x))∣∣ ∣∣ωj (t, x)∣∣ dx+2
m∑i=1
∫�
∣∣∣d̄ij∣∣∣∫ t
−∞k̄ij (t − s)
∣∣g̃i (ei (s, x))∣∣ ∣∣ωj (t, x)∣∣ dsdx − 2∫
�
n∑k=1
ρjk |ωk (t, x)|∣∣ωj (t, x)∣∣dx.
(19)
Consider the following Lyapunov functional
V (t) =∫
�
m∑i=1
wi[nar−1i |ei (t, x)|re2αt +
n∑j=1
∣∣∣b̃ji∣∣∣rnr eθ1 − μθ∫ tt−θji(t)
e2αξ∣∣∣f̃j (ωj (ξ , x))∣∣∣rdξ
+n∑j=1
∣∣∣b̄ji∣∣∣rnrβ rji∫ +∞0
kji (s)∫ tt−s
e2α(s+ξ )∣∣∣f̃j (ωj (ξ , x))∣∣∣rdξds
⎤⎦ dx
+∫
�
n∑j=1
wm+j[mcr−1j
∣∣ωj (t, x)∣∣re2αt + m∑i=1
∣∣∣d̃ij∣∣∣rmr eτ1 − μτ∫ tt−τij(t)
e2αξ∣∣g̃i (ei (ξ , x))∣∣rdξ
+m∑i=1
∣∣∣d̄ij∣∣∣rmrγ rij∫ +∞0
k̄ij (s)∫ tt−s
e2α(s+ξ )∣∣g̃i (ei (ξ , x))∣∣rdξds
]dx.
(20)
Its upper Dini-derivative along the solution to system (8) can
be calculated as
D+V (t) ≤∫
�
m∑i=1
wi
[rnar−1i |ei (t, x)|r−1
∂ |ei (t, x)|∂t
e2αt + 2αe2αtnar−1i |ei (t, x)|r
+e2αtn∑j=1
∣∣∣b̃ji∣∣∣rnr eθ1 − μθ∣∣∣f̃j (ωj (t, x))∣∣∣r + e2αt n∑
j=1
∣∣∣b̄ji∣∣∣rnrβ rji∫ +∞0
e2αskji (s)∣∣∣f̃j (ωj (t, x))∣∣∣rds
−n∑j=1
∣∣∣b̃ji∣∣∣rnr eθ1 − μθ(1 − θ̇ji (t)
)e2α(t−θji(t))
∣∣∣f̃j (ωj (t − θji (t) , x))∣∣∣r
−e2αtn∑j=1
∣∣∣b̄ji∣∣∣rnrβ rji∫ +∞0
kji (s)∣∣∣f̃j (ωj (t − s, x))∣∣∣rds
⎤⎦ dx
+∫
�
n∑j=1
wm+j
[rmcr−1j
∣∣ωj (t, x)∣∣r−1 ∂∣∣ωj (t, x)∣∣
∂te2αt + 2αe2αtmcr−1j
∣∣ωj (t, x)∣∣r
+e2αtm∑i=1
∣∣∣d̃ij∣∣∣rmr eτ1 − μτ∣∣g̃i (ei (t, x))∣∣r
−m∑i=1
∣∣∣d̃ij∣∣∣rmr eτ1 − μτ e2α(t−τij(t))(1 − τ̇ij (t)
)∣∣g̃i (ei (t − τij (t) , x))∣∣r
+e2αtm∑i=1
∣∣∣d̄ij∣∣∣rmrγ rij∫ +∞0
e2αsk̄ij (s)∣∣g̃i (ei (t, x))∣∣rds
−e2αtm∑i=1
∣∣∣d̄ij∣∣∣rmrγ rij∫ +∞0
k̄ij (s)∣∣g̃i (ei (t − s, x))∣∣rds
]dx
(21)
Zhang and Li Boundary Value Problems 2012,
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-
From (21) and Young inequality, we can conclude
D+V (t) ≤∫
�
e2αtm∑i=1
[wi
(−rnar−1i Diλ1 − rnari + 2 (r − 1)n∑j=1
ari + 2αnar−1i + (r − 1)
m∑k=1,i�=k
ari
−rnμiiar−1i + (r − 1)n∑j=1
ariβ−
rr − 1
ji
∫ t−∞
kji (t − s) ds
⎞⎟⎠ + n∑
j=1
wm+jmr(∣∣dij∣∣r(Lgi )r
+∣∣∣d̃ij∣∣∣r eτ1 − μτ
(Lgi
)r+∣∣∣d̄ij∣∣∣rγ rij
∫ +∞0
e2αsk̄ij (s)(Lgi
)rds
)+ nr
m∑k=1,i�=k
|μki|rwk⎤⎦ |ei (t, x)|rdx
+∫
�
e2αtn∑j=1
[wm+j
(−rmcr−1j D∗j λ1 − rmcrj − rmρjjcr−1j + 2 (r − 1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj
+ (r − 1)m∑i=1
crj γ−
rr − 1
ij
∫ t−∞
k̄ij (t − s) ds +2αmcr−1j)+
m∑i=1
winr(∣∣bji∣∣r(Lfj )r
+∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj )r
∫ +∞0
e2αskji (s) ds)+mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m⎤⎦ ∣∣ωj (t, x)∣∣rdx
(22)
From (10), we can conclude
D+V (t) ≤ 0, and so V (t) ≤ V (0) , t ≥ 0 (23)
Since
V (0) =∫
�
m∑i=1
wi[nar−1i |ei (0, x)|r
+n∑j=1
∣∣∣b̃ji∣∣∣rnr eθ1 − μθ∫ 0
−θji(t)
∣∣∣f̃j (ωj (ξ , x))∣∣∣rdξ
+n∑j=1
∣∣∣b̄ji∣∣∣rnrβ rji∫ +∞0
kji (s)∫ 0
−se2α(s+ξ )
∣∣∣f̃j (ωj (ξ , x))∣∣∣rdξds⎤⎦ dx
+∫
�
n∑j=1
wm+j[mcr−1j
∣∣ωj (0, x)∣∣r + m∑i=1
∣∣∣d̃ij∣∣∣rmr eτ1 − μτ∫ 0
−τij(t)
∣∣g̃i (ei (ξ , x))∣∣rdξ+
m∑i=1
∣∣∣d̄ij∣∣∣rmrγ rij∫ +∞0
k̄ij (s)∫ 0
−se2α(s+ξ )
∣∣g̃i (ei (ξ , x))∣∣rdξds]dx
≤{max1≤i≤m
{wi} + max1≤j≤n
{wm+j
}max1≤j≤n
[m∑i=1
∣∣∣d̄ij∣∣∣rmr(Lgi )rγ rij∫ +∞0
k̄ij (s) se2αsds
]
+ max1≤j≤n
{wm+j
}max1≤j≤n
[m∑i=1
∣∣∣d̃ij∣∣∣r(Lgi )rmr eτ τ1 − μτ]}
‖ϕu (s, x) − ψu (s, x)‖r
+{max1≤j≤n
{wm+j
}+ max
1≤i≤m{wi} max
1≤i≤m
⎡⎣ n∑
j=1
∣∣∣b̄ji∣∣∣rnr(Lfj )rβ rji∫ +∞0
se2αskji (s)ds
⎤⎦
+ max1≤i≤m
{wi} max1≤i≤m
⎡⎣ n∑
j=1
∣∣∣b̃ji∣∣∣rnr(Lgj )r eθ θ1 − μθ⎤⎦⎫⎬⎭ ‖ϕv (s, x) − ψv (s,
x)‖r
(24)
Noting that
e2αt(
min1≤i≤m+n
wi
)(‖e (t, x)‖ + ‖ω (t, x)‖) ≤ V (t) , t ≥ 0. (25)
Zhang and Li Boundary Value Problems 2012,
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-
Let
β = max{max1≤i≤m
{wi}+ max1≤j≤n
{wm+j
}max1≤j≤n
[m∑i=1
∣∣∣d̄ij∣∣∣rmr(Lgi )rγ rij∫ +∞0
k̄ij (s) se2αsds
]
+ max1≤j≤n
{wm+j
}max1≤j≤n
[m∑i=1
∣∣∣d̃ij∣∣∣r(Lgi )rmr eτ τ1 − μτ],
max1≤j≤n
{wm+j
}+ max
1≤i≤m{wi} max
1≤i≤m
⎡⎣ n∑
j=1
∣∣∣b̄ji∣∣∣rnr(Lfj )rβ rji∫ +∞0
se2αskji (s)ds
⎤⎦
+ max1≤i≤m
{wi} max1≤i≤m
[n∑j=1
∣∣∣b̃ji∣∣∣rnr(Lgj )r eθ θ1 − μθ]}/
min1≤i≤m+n
{wi}.
Clearly, b ≥ 1.It follows that
‖e (t, x)‖ + ‖ω (t, x)‖ ≤ βe−2αt (‖ϕu (s, x) − ψũ (s, x)‖ + ‖ϕv
(s, x) − ψṽ (s, x)‖) . (26)
for any t ≥ 0 where b ≥ 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 irrele-
vant to the diffusion term. Therefore, Theorem 1 is essentially
new and more effective-
ness 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 synchroni-
zation 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 sys-
tem 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
u̇i = −pi (ui (t)) +n∑j=1
bjifj(vj (t)
)+
n∑j=1
b̃jifj(vj
(t − θji (t)
))
+n∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(vj (s)
)ds + Ii (t) ,
v̇j = −qj(vj (t)
)+
m∑i=1
dijgi (ui (t)) +m∑i=1
d̃ijgi(ui
(t − τij (t)
))
+m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi (ui (s)) ds + Jj (t)
(27)
Zhang and Li Boundary Value Problems 2012,
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-
and the corresponding response system (4) will become the
following form
˙̃ui (t) = −pi(ũi (t)
)+
n∑j=1
bjifj(ṽj (t)
)+
n∑j=1
b̃jifj(ṽj
(t − θji (t)
))
+n∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(ṽj (s)
)ds + Ii (t) + σi (t) ,
˙̃vj (t) = −qj(ṽj (t)
)+
m∑i=1
dijgi(ũi (t)
)+
m∑i=1
d̃ijgi(ũi
(t − τij (t)
))
+m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi(ũi (s)
)ds + Jj (t) + ϑj (t) .
(28)
Define the synchronization error signal ei (t) = ui (t) − ũi
(t) ,ωj (t) = vj (t) − ṽj (t),then the error dynamics between
systems (27) and (28) can be expressed by
ėi (t) = −p̃i (ei (t)) +n∑j=1
bjif̃j(ωj (t)
)+
n∑j=1
b̃ji f̃j(ωj
(t − θji (t)
))
+n∑j=1
b̄ji
∫ t−∞
kji (t − s) f̃j(ωj (s)
)ds − σi (t) ,
ω̇j (t) = −q̃j(ωj (t)
)+
m∑i=1
dijg̃i (ei (t)) +m∑i=1
d̃ijg̃i(ei
(t − τij (t)
))
+m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) g̃i (ei (s)) ds − ϑj (t) ,
(29)
We consider the following control inputs strategy
σi (t) =m∑k=1
μikek (t) ,ϑj (t) =n∑
k=1
ρjkωk (t) , i = 1, 2, ...,m, j = 1, 2, ..., n. (30)
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 wi
> 0 (i = 1, 2,...,n+m), r ≥ 2,
gij > 0, bji > 0 such that the controller gain matrices μ
and r in (9) satisfy
wi
⎛⎜⎝−rnari − rnμiiar−1i + 2 (r − 1)
n∑j=1
ari + (r − 1)n∑j=1
ariβ−
rr − 1
ji + (r − 1)m∑
k=1,i�=kari
⎞⎟⎠
+n∑j=1
wm+jmr(∣∣dij∣∣r(Lgi )r + ∣∣∣d̃ij∣∣∣r eτ1 − μτ
(Lgi
)r+∣∣∣d̄ij∣∣∣rγ rij(Lgi )r) + nr
m∑k=1,i�=k
|μki|rwk < 0
and
wm+j
⎛⎜⎝−rmcrj − rmρjjcr−1j + 2 (r − 1)
m∑i=1
crj + (r − 1)n∑
k=1,j�=kcrj + (r − 1)
m∑i=1
crj γ−
rr − 1
ij
⎞⎟⎠
+m∑i=1
winr(∣∣bji∣∣r(Lfj )r + ∣∣∣b̃ji∣∣∣r eθ1 − μθ
(Lfj
)r+∣∣∣b̄ji∣∣∣rβ rji(Lfj)r) +mr
n∑k=1,j�=k
∣∣ρkj∣∣rwk+m < 0,(31)
Zhang and Li Boundary Value Problems 2012,
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-
in which i = 1, 2,..., m, j = 1, 2,..., n, Lfj and Lgi are
Lipschitz constants.
4. Illustration exampleTo illustrate the effectiveness of our
criterion, we give the following example.
Example 1. Consider the following system on
∂ui∂t
=∂2ui∂x2
− aiui (t, x) +n∑j=1
bjifj(vj (t, x)
)+
n∑j=1
b̃jifj(vj
(t − θji (t) , x
))
+n∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(vj (s, x)
)ds + Ii,
∂vj∂t
=∂2vj∂x2
− cjvj (t, x) +m∑i=1
dijgi (ui (t, x)) +m∑i=1
d̃ijgi(ui
(t − τij (t) , x
))
+m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi (ui (s, x)) ds + Jj,
∂ui∂t
=∂2ui∂x2
− aiui (t, x) +n∑j=1
bjifj(vj (t, x)
)+
n∑j=1
b̃jifj(vj
(t − θji (t) , x
))
+n∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(vj (s, x)
)ds + Ii,
∂vj∂t
=∂2vj∂x2
− cjvj (t, x) +m∑i=1
dijgi (ui (t, x)) +m∑i=1
d̃ijgi(ui
(t − τij (t) , x
))
+m∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi (ui (s, x)) ds + Jj,
(32)
and
∂ũi (t, x)∂t
=∂2ũi (t, x)
∂x2− aiũi (t, x) +
2∑j=1
bjifj(ṽj (t, x)
)+
2∑j=1
b̃jifj(ṽj
(t − θji (t) , x
))
+2∑j=1
b̄ji
∫ t−∞
kji (t − s) fj(ṽj (s, x)
)ds + Ii (t) +
2∑k=1
μikek (t, x) ,
∂ ṽj (t, x)
∂t=
∂2ṽj (t, x)
∂x2− cjṽj (t, x) +
2∑i=1
dijgi(ũi (t, x)
)+
2∑i=1
d̃ijgi(ũi
(t − τij (t) , x
))
+2∑i=1
d̄ij
∫ t−∞
k̄ij (t − s) gi(ũi (s, x)
)ds + Jj (t) +
2∑k=1
ρjkωk (t, x) ,
(33)
where n = m = r = 2, kji (t) = k̄ij (t) = te−t , fj (η) = gi (η)
=12
(|η + 1| + |η − 1|) ,
Lfj = Lf̄j = L
gi = L
ḡi = 1, i, j = 1, 2. λ1 = 5, τ = θ = ln 2. a1 = 1, a2 = 2, c1 =
2, c2 = 1,
μτ = μθ = 0.2, d11 = 0.5, d12 = 1, d21 = 0.5, d22 = 0.2, d̄11 =
0.2, d̄12 = 0.6, d̄21 = 0.5,
d̄22 = 0.8, b11 = 0.5, b12 = 0.6, b21 = 1, b22 = −0.8, b̄11 =
−1, b̄12 = 0.2, b̄21 = 0.5, b̄22 = 0.4.
Zhang and Li Boundary Value Problems 2012,
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Page 12 of 14
-
μ11 = 0.5,μ12 = 0.3,μ21 = 0.7,μ22 = 0.1, ρ11 = 0.6,ρ12 = 2,ρ21 =
1,ρ22 = 0.4.
By simple calculation with w1 = w2 = w3 = w4 = 1, b11 = b12 =
b21 = b22 = 1,and g11 = g12 = g21 = g22 = 1, we get
−rnar−11 λ1 − rnar1 − rnμ11ar−11 + 2 (r − 1)2∑j=1
ar1 + (r − 1)2∑j=1
ar1β−
rr − 1
j1 + (r − 1) ar1
+2∑j=1
mr(∣∣d1j∣∣r(Lg1)r + ∣∣∣d̄1j∣∣∣rγ r1j(Lḡ1)r
⎞⎠ + nr|μ12|r = −12.04 < 0,
−rnar−12 λ1 − rnar2 − rnμ22ar−12 + 2 (r − 1)2∑j=1
ar2 + (r − 1)2∑j=1
ar2β−
rr − 1
j2 + (r − 1) ar2
+2∑j=1
mr(∣∣d2j∣∣r(Lg2)r +∣∣∣d̄2j∣∣∣rγ r2j(Lḡ2)r) + nr|μ21|r = −22.12
< 0,
−rmcr−11 λ1 − rmcr1 − rmρ11cr−11 + 2 (r − 1)2∑i=1
cr1 + (r − 1) cr1 + (r − 1)2∑i=1
cr1γ−
r
r − 1i1
+2∑i=1
nr(|b1i|r
(Lf1
)r+∣∣∣b̄1i∣∣∣rβ r1i(Lf̄1)r
)+mr|ρ12|r = −9.8 < 0
and
−rmcr−12 λ1 − rmcr2 − rmρ22cr−12 + 2 (r − 1)2∑i=1
cr2 + (r − 1) cr2 + (r − 1)2∑i=1
cr2γ−
rr − 1
i2
+2∑i=1
nr(|b2i|r
(Lf2
)r+∣∣∣b̄2i∣∣∣rβ r2i(Lf̄2)r
)+mr|ρ21|r = −9.4 < 0,
Hence, it follows from Theorem 1 that (32) and (33) are globally
exponentially
synchronized.
5. ConclusionsIn 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 diffu-
sion 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.
AcknowledgementsThis study was partially supported by the
National Natural Science Foundation of China under Grant No.
60974139and 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 researchprojects in Shannxi Province
Department of Education under Grant No. 2010JK896.
Author details1School of Science, Xidian University, Shaan Xi
Xi’an 710071, P.R. China 2Institute of Maths and Applied
Mathematics,Xianyang Normal University, Xianyang, ShaanXi 712000,
P.R. China
Zhang and Li Boundary Value Problems 2012,
2012:2http://www.boundaryvalueproblems.com/content/2012/1/2
Page 13 of 14
-
Authors’ contributionsWZ designed and performed all the steps of
proof in this research and also wrote the paper. JL participated in
thedesign of the study and suggest many good ideas that made this
paper possible and helped to draft the firstmanuscript. All authors
read and approved the final manuscript.
Competing interestsThe authors declare that they have no
competing interests.
Received: 25 October 2011 Accepted: 13 January 2012 Published:
13 January 2012
References1. Aihara, K, Takabe, T, Toyoda, M: Chaotic neural
networks. Phys Lett A. 144, 333–340 (1990).
doi:10.1016/0375-9601(90)
90136-C2. Kwok, T, Smith, K: Experimental analysis of chaotic
neural network models for combinatorial optimization under a
unifying framework. Neural Netw. 13, 731–744 (2000).
doi:10.1016/S0893-6080(00)00047-23. Yu, W, Cao, J: Cryptography
based on delayed chaotic neural networks. Phys Lett A. 356, 333–338
(2006). doi:10.1016/j.
physleta.2006.03.0694. Cheng, C, Liao, T, Yan, J, Wang, CH:
Exponential synchronization of a class of neural networks with
time-varying delays.
IEEE Trans Syst Man Cybern B. 36, 209–215 (2006)5. Kosko, B:
Bi-directional associative memories. IEEE Trans Syst Man Cybern.
18(1):49–60 (1988). doi:10.1109/21.870546. Cao, J, Wang, L:
Exponential stability and periodic oscillatory solution in BAM
networks with delays. IEEE Trans Neural
Netw. 13(2):457–463 (2002). doi:10.1109/72.9914317. 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)8. Lou, X,
Cui, B: Stochastic exponential stability for Markovian jumping BAM
neural networks with time-varying delays.
IEEE Trans Syst Man Cybern. 37, 713–719 (2007)9. Park, JH: A
novel criterion for global asymptotic stability of BAM neural
networks with time delays. Chaos Solitons
Fractals. 29(2):446–453 (2006).
doi:10.1016/j.chaos.2005.08.01810. Park, JH, Kwon, OM:
Delay-dependent stability criterion for bidirectional associative
memory neural networks with
interval time-varying delays. Mod Phys Lett B. 23(1):35–46
(2009). doi:10.1142/S021798490901780711. Park, JH, Park, CH, Kwon,
OM, Lee, SM: A new stability criterion for bidirectional
associative memory neural networks of
neutral-type. Appl Math Comput. 199(2):716–722 (2008).
doi:10.1016/j.amc.2007.10.03212. Park, JH, Kwon, OM: On improved
delay-dependent criterion for global stability of bidirectional
associative memory
neural networks with time-varying delays. Appl Math Comput.
199(2):435–446 (2008). doi:10.1016/j.amc.2007.10.00113. Zhu, QX,
Cao, J: Exponential stability analysis of stochastic
reaction-diffusion Cohen-Grossberg neural networks with
mixed delays. Neurocomputing. 74, 3084–3091 (2011).
doi:10.1016/j.neucom.2011.04.03014. Song, Q, Cao, J: Global
exponential stability and existence of periodic solutions in BAM
with delays and reaction-
diffusion terms. Chaos Solitons Fractals. 23(2):421–430 (2005).
doi:10.1016/j.chaos.2004.04.01115. Cui, B, Lou, X: Global
asymptotic stability of BAM neural networks with distributed delays
and reaction-diffusion terms.
Chaos Solitons Fractals. 27(5):1347–1354 (2006).
doi:10.1016/j.chaos.2005.04.11216. Hu, C, Jiang, HJ, Teng, ZD:
Impulsive control and synchronization for delayed neural networks
with reaction-diffusion
terms. IEEE Trans Neural Netw. 21(1):67–81 (2010)17. Wang, Z,
Zhang, H: Global asymptotic stability of reaction-diffusion
Cohen-Grossberg neural network with continuously
distributed delays. IEEE Trans Neural Netw. 21(1):39–49
(2010)18. Wang, L, Zhang, R, Wang, Y: Global exponential stability
of reaction-diffusion cellular neural networks with S-type
distributed time delays. Nonlinear Anal Real World Appl.
10(2):1101–1113 (2009). doi:10.1016/j.nonrwa.2007.12.00219.
Balasubramaniam, P, Vidhya, C: Global asymptotic stability of
stochastic BAM neural networks with distributed delays
and reaction-diffusion terms. J Comput Appl Math. 234, 3458–3466
(2010). doi:10.1016/j.cam.2010.05.00720. Lu, J, Lu, L: Global
exponential stability and periodicity of reaction-diffusion
recurrent neural networks with distributed
delays and Dirichlet boundary conditions. Chaos Solitons
Fractals. 39(4):1538–1549 (2009).
doi:10.1016/j.chaos.2007.06.040
21. Song, Q, Zhao, Z, Li, YM: Global exponential stability of
BAM neural networks with distributed delays and reaction-diffusion
terms. Phys Lett A. 335(2-3):213–225 (2005).
doi:10.1016/j.physleta.2004.12.007
22. Zhang, W, Li, J: Global exponential stability of
reaction-diffusion neural networks with discrete and distributed
time-varying delays. Chin Phys B. 20(3):030701 (2011).
doi:10.1088/1674-1056/20/3/030701
23. Liao, XX, Yang, SZ, Cheng, SJ, Fu, YL: Stability of
generalized networks with reaction-diffusion terms. Sci China
(Series F).44, 87–94 (2001). doi:10.1007/BF02884813
24. Lou, X, Cui, B: Asymptotic synchronization of a class of
neural networks with reaction-diffusion terms and
time-varyingdelays. Comput Math Appl. 52, 897–904 (2006).
doi:10.1016/j.camwa.2006.05.013
25. Wang, Y, Cao, J: Synchronization of a class of delayed
neural networks with reaction-diffusion terms. Phys. Lett A.
369,201–211 (2007). doi:10.1016/j.physleta.2007.04.079
26. Sheng, L, Yang, H, Lou, X: Adaptive exponential
synchronization of delayed neural networks with
reaction-diffusionterms. Chaos Solitons Fractals. 40, 930–939
(2009). doi:10.1016/j.chaos.2007.08.047
27. Wang, K, Teng, Z, Jiang, H: Global exponential
synchronization in delayed reaction-diffusion cellular neural
networkswith the Dirichlet boundary conditions. Math Comput Model.
52, 12–24 (2010). doi:10.1016/j.mcm.2009.05.038
28. Temam, R: Infinite Dimensional Dynamical Systems in
Mechanics and Physics. Springer-Verlag, New York (1998)
doi:10.1186/1687-2770-2012-2Cite this article as: Zhang and Li:
Global exponential synchronization of delayed BAM neural networks
withreaction-diffusion terms and the Neumann boundary conditions.
Boundary Value Problems 2012 2012:2.
Zhang and Li Boundary Value Problems 2012,
2012:2http://www.boundaryvalueproblems.com/content/2012/1/2
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http://www.ncbi.nlm.nih.gov/pubmed/11152205?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/11152205?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/18244446?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/18334360?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/18334360?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/19932996?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/19932996?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/19923046?dopt=Abstracthttp://www.ncbi.nlm.nih.gov/pubmed/19923046?dopt=Abstract
Abstract1. Introduction2. Model description and preliminaries3.
Main results4. Illustration example5.
ConclusionsAcknowledgementsAuthor detailsAuthors'
contributionsCompeting interestsReferences