Exponential stability for fuzzy BAM cellular neural networks with ...

Xu and Li Advances in Difference Equations (2016) 2016:276 DOI 10.1186/s13662-016-0978-0

RESEARCH

Open Access

Exponential stability for fuzzy BAM cellular neural networks with distributed leakage delays and impulses Changjin Xu1* and Peiluan Li2 *

Correspondence: [email protected] Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang, 550004, P.R. China Full list of author information is available at the end of the article 1

Abstract This paper is concerned with a class of fuzzy BAM cellular neural networks with distributed leakage delays and impulses. By applying differential inequality techniques, we establish some sufficient conditions which ensure the exponential stability of such fuzzy BAM cellular neural networks. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this article are completely new and complement the previously known studies. MSC: 34K20; 34K13; 92B20 Keywords: fuzzy BAM cellular neural networks; exponential stability; distributed leakage delay; impulse

1 Introduction In recent years, a lot of authors pay much attention to dynamics of bidirectional associative memory (BAM) neural networks due to their potential application prospect in many disciplines such as pattern recognition, automatic control engineering, optimization problems, image processing, speed detection of moving objects and so on [–]. Since time delays usually occur in neural networks due to the finite switching of amplifiers in practical implementation, and the time delay may result in oscillation and instability of system, many researchers investigate the dynamical nature of delayed BAM neural networks. For example, Xiong et al. [] discussed the stability of two-dimensional neutral-type CohenGrossberg BAM neural networks, Zhang et al. [] investigated the global stability and synchronization of Markovian switching neural networks with stochastic perturbation and impulsive delay. Some novel generic criteria for Markovian switching neural networks with stochastic perturbation and impulsive delay are derived by establishing an extended Halanay differential inequality on impulsive dynamical systems, in addition, some sufficient conditions ensuring synchronization are established, Wang et al. [] made a detailed analysis on the exponential stability of delayed memristor-based recurrent neural networks with impulse effects. By using an impulsive delayed differential inequality and Lyapunov function, several exponential and uniform stability criteria of the impulsive delayed memristor-based recurrent neural networks are obtained. Li et al. [] studied the existence and stability of pseudo almost periodic solution for neutral type high-order Hop© 2016 Xu and Li. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Xu and Li Advances in Difference Equations (2016) 2016:276

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field neural networks with delays in leakage terms on time scales. Applying the exponential dichotomy of linear dynamic equations on time scales, a fixed point theorem, and the theory of calculus on time scales, the authors established some sufficient conditions for the existence and global exponential stability of pseudo almost periodic solutions for the model. For more related work, we refer the reader to [–]. Some authors argue that a typical time delay called leakage (or ‘forgetting’) delay may occur in the negative feedback term of the neural networks model (these terms are variously known as forgetting or leakage terms) and have a great impact on the dynamics of neural networks [–]. For example, time delay in the stabilizing negative feedback term has a tendency to destabilize a system [], Balasubramanianm et al. [] pointed out that the existence and uniqueness of the equilibrium point are independent of time delays and initial conditions. In real world, uncertainty or vagueness is unavoidable. Thus it is necessary to introduce the fuzzy operator into the neural networks. In , Balasubramaniam et al. [] considered the global asymptotic stability of the following BAM fuzzy cellular neural networks with time delay in the leakage term, discrete and unbounded distributed delays: ⎧ xi (t) = –ai xi (t – σ ) + m ⎪ j= aij (t)fj (yj (t)) ⎪ ⎪ m ⎪ m ⎪ b (t)f (y (t + ⎪ j j – τ (t))) + j= ij j= cij (t)ωj ⎪ ⎪ t t m ⎪ ⎪ + j= αij –∞ kj (t – s)fj (yj (s)) ds + m ⎪ j= βij –∞ kj (t – s)fj (yj (s)) ds ⎪ ⎪ m ⎨ + m t > , i = , , . . . , n, j= Tij ωj + j= Hij ωj + Ii , n ⎪ yj (t) = –bj yj (t – σ ) + i= a¯ ji (t)gi (xi (t)) + ni= b¯ ji (t)gi (xi (t – ρ(t))) ⎪ ⎪ t ⎪ ⎪ ⎪ + ni= c¯ ji (t)ω¯ i + ni= α¯ ji –∞ ki (t – s)gi (xi (s)) ds ⎪ ⎪ ⎪ t ⎪ ⎪ + ni= β¯ji –∞ ki (t – s)gi (xi (s)) ds ⎪ ⎪ n ⎩ ¯ ji ω¯ i + Jj , t > , j = , , . . . , m. + i= T¯ ji ω¯ i + ni= H

(.)

The meaning of all the parameters of system (.) can be found in []. By applying the quadratic convex combination method, reciprocal convex approach, Jensen integral inequality, and linear convex combination technique, Balasubramaniam et al. [] obtained several sufficient conditions to ensure the global asymptotic stability of the equilibrium point of system (.). Considering that time-varying delays in the leakage terms inevitably occur in electronic neural networks due to the unavoidable finite switching speed of amplifiers [], Li et al. [] considered the existence and exponential stability of an equilibrium point for the following fuzzy BAM neural networks with time-varying delays in leakage terms on time scales: ⎧ xi (t) = –ai xi (t – σi (t)) + m ⎪ j= cji (t)fj (yj (t – τji (t))) ⎪ ⎪ m m ⎪ ⎪ + j= αji fj (yj (t – τji (t))) + m ⎪ j= Tji μj + j= βji fj (yj (t – τji (t))) ⎪ ⎪ ⎨ H μ + I , t ∈ T, i = , , . . . , n, + m i j= ji j n ⎪ y (t) = –b y (t – η (t)) + j j j ⎪ j i= dij (t)gi (xi (t – σij (t))) ⎪ m n n ⎪ ⎪ ⎪ + p g (x (t – σ ij i i ij (t))) + ⎪ i= Fij νi + i= qij gi (xi (t – σij (t))) ⎪ j= ⎩ n + i= Gij νi + Jj , t ∈ T, j = , , . . . , m,

(.)

where T is a time scale. Applying fixed point theorem and differential inequality techniques, Li et al. [] obtained the sufficient condition which ensures the existence and global exponential stability of an equilibrium point for system (.). Noticing that the im-


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pulsive perturbations usually occur in neural networks, Li and Li [] investigated the exponential stability of the following BAM fuzzy cellular neural networks with time-varying delays in leakage terms and impulses: ⎧ m ⎪ xi (t) = –ai (t)xi (t – αi (t)) + m ⎪ j= aij (t)fj (yj (t)) + j= bij (t)fj (yj (t – τ (t))) ⎪ m m ⎪ t ⎪ + c (t)ω + α (t) k (t – s)f (y ⎪ ij j j j (s)) ds j= ij ⎪ –∞ j ⎪ t mj= ⎪ ⎪ + j= βij (t) –∞ kj (t – s)fj (yj (s)) ds ⎪ ⎪ ⎪ m ⎪ ⎪ + m t ≥ , t = tk , i = , , . . . , n, ⎪ j= Tij ωj + j= Hij ωj + Ai (t), ⎪ ⎪ ⎨ x (t ) = I (x (t )), i = , , . . . , n, k = , , . . . , i k k i k n n ⎪ (t) = –b (t)y y j j (t – βj (t)) + ⎪ j i= dji (t)gi (xi (t)) + i= pji (t)gi (xi (t – ρ(t))) ⎪ ⎪ t n n ⎪ ⎪ q (t)μ + γ (t) k (t – s)g (x + ji i i (s)) ds ⎪ i= ji –∞ i ⎪ it ni= ⎪ ⎪ ⎪ + i= ηji (t) –∞ ki (t – s)gi (xi (s)) ds ⎪ ⎪ n ⎪ ⎪ ⎪ Rji μi + ni= Sji μi + Bj (t), t ≥ , t = tk , j = , , . . . , m, + i= ⎪ ⎪ ⎩ yj (tk ) = Jk (yj (tk )), j = , , . . . , m, k = , , . . . .

(.)

By applying differential inequality techniques, Li and Li [] established some sufficient conditions which guarantee the exponential stability of model (.). Here we would like to point out that neural networks usually have spatial natures due to the presence of an amount of parallel pathways of a variety of axon sizes and lengths. It is reasonable to introduce continuously distributed delays over a certain duration of time such that the distant past has less influence compared with the recent behavior of the state [, ]. Inspired by the analysis above, in this paper we consider the following fuzzy BAM neural networks with distributed leakage delays and impulses: ⎧ ∞ m ⎪ xi (t) = –ai (t)  hi (s)xi (t – s) ds + m bij (t)fj (yj (t – τ (t))) ⎪ j= aij (t)fj (yj (t)) + ⎪ t j= ⎪ t m m ⎪ ⎪ + α (t) k (t – s)f (y (s)) ds + β (t) k ij j j j ij j= ⎪ –∞ –∞ j (t – s)fj (yj (s)) ds ⎪ j= m m ⎪ m ⎪ + j= Tij ωj + j= Hij ωj + j= cij (t)ωj ⎪ ⎪ ⎪ ⎪ ⎪ + Ai (t), t ≥ , t = tk , i = , , . . . , n, ⎪ ⎪ ⎪ ⎨ x (t ) = I (x (t )), i = , , . . . , n, k = , , . . . , i k k i k ∞ (.) n n ⎪ (t) = –b (t) y j j (t – s) ds + ⎪ j i= dji (t)gi (xi (t)) + i= pji (t)gi (xi (t – ρ(t)))  lj (s)y ⎪ t t ⎪ ⎪ ⎪ + ni= γji (t) –∞ ki (t – s)gi (xi (s)) ds + ni= ηji (t) –∞ ki (t – s)gi (xi (s)) ds ⎪ ⎪ ⎪ ⎪ ⎪ + ni= Rji μi + ni= Sji μi + ni= qji (t)μi ⎪ ⎪ ⎪ ⎪ + Bj (t), t ≥ , t = tk , j = , , . . . , m, ⎪ ⎪ ⎪ ⎩ yj (tk ) = Jk (yj (tk )), j = , , . . . , m, k = , , . . . , which is a revised version of model (.). Here xi (t) and yj (t) are the states of the ith neuron and the jth neuron at time t, gi (t) and fj (t) denote the activation functions of the ith neuron and the jth neuron at time t, μi and ωj denote the inputs of the ith neuron and the jth neuron, Ai (t) and Bj (t) denote the bias of the ith neuron and the jth neuron at time t, ai (t) and bj (t) represent the rates with which the ith neuron and the jth neuron at time t will reset their potential to the resting state in isolation when disconnected from the networks and external inputs, aij (t), bij (t), dji (t), and pji (t) denote the connection weights of the feedback template at time t and cij (t), qji (t) denote the connection weights of the feedforward template at time t, γji (t) and ηji (t) denote the connection weights of the delays fuzzy feedback MIN template at time t and the delays fuzzy feedback MAX template at time t, Tij , Rij , and Hji , Sij are the elements of the fuzzy feedforward MIN


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template and fuzzy feedforward MAX template, and denote the fuzzy AND and fuzzy OR operators,  ≤ τ (t) ≤ τ and  ≤ ρ(t) ≤ ρ denote the transmission delays at time t, xi (tk ) = xi (tk+ ) – xi (tk– ), yj (tk ) = yj (tk+ ) – yj (tk– ) are the impulses at moments tk and t < t < · · · is a strictly increasing sequence such that limk→∞ tk = +∞, kj (s) ≥ , and +∞ +∞ ki (s) ≥  are the feedback kernels and satisfy  kj (s) ds = ,  ki (s) ds = , i = , , . . . , n, j = , , . . . , m. Our main object of this article is by applying differential inequality techniques to analyze the exponential stability of model (.). We expect that this study of the exponential stability of model (.) has important theoretical value and tremendous potential for application in designing the BAM cellular neural networks with distributed leakage delays. Let R and R+ denote the set of all real numbers and nonnegative real numbers, respectively. For the sake of simplification, we introduce the notations as follows: f + = supt∈R |f (t)|, f – = inft∈R |f (t)|, where f : R → R is a continuous function. The initial value of system (.) is given by xi (s) = ϕi (s),

yj (s) = ψj (s),

s ∈ (–∞, ],

(.)

where ϕi (s), ψj (s) ∈ C((–∞, ], R), i = , , . . . , n, j = , , . . . , m. Throughout this paper, we assume that the following conditions are satisfied. f (H) For i = , , . . . , n, j = , , . . . , m, fj , gi ∈ C(R, R) and there exist positive constants Lj g and Li such that

fj (u) – fj (v) ≤ Lf |u – v|, j

gi (u) – gi (v) ≤ Lg |u – v| i

for u, n ∈ R. (H) For i = , , . . . , n, j = , , . . . , m, ai (t) >  and bj (t) >  for t ∈ R. The remainder of the paper is organized as follows: in Section , we introduce a useful definition and a lemma. In Section , some sufficient conditions which ensure the exponential stability of model (.) are established. In Section , an example which illustrates the theoretical findings is given. A brief conclusion is drawn in Section .

2 Preliminaries In order to obtain the main result of this paper, we shall first state a definition and a lemma which will be useful in proving the main result. Definition . Let u∗ = (x∗ , x∗ , . . . , x∗n , y∗ , y∗ , . . . , y∗m )T be a solution of system (.) with initial value φ ∗ = (ϕ∗ , ϕ∗ , . . . , ϕn∗ , ψ∗ , ψ∗ , . . . , ψm∗ )T , there exists a constant λ >  that, for every solution u(t) = (x (t), x (t), . . . , xn (t), y (t), y (t), . . . , ym (t))T of equation (.) with initial value φ(s) = (ϕ (s), ϕ (s), . . . , ϕn (s), ψ (s), ψ (s), . . . , ψm (s))T , satisfies xi (t) – x∗i (t) = O e–λt ,

yj (t) – y∗j (t) = O e–λt ,

where i = , , . . . , n, j = , , . . . , m. Lemma . [] Let x and y be two states of system (.). Then

n

n n

αij (t)

gj (x) – gj (y)

αij (t)gj (y) ≤

αij (t)gj (x) –

j=

j=

j=


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and

n

n n

βij (t)

gj (x) – gj (y) . β (t)g (x) – β (t)g (y) ≤

ij j ij j

j=

j=

j=

3 Exponential stability In this section, we will consider the exponential stability of system (.). Theorem . Let u∗ = (x∗ , x∗ , . . . , x∗n , y∗ , y∗ , . . . , y∗m )T be a solution of system (.) with initial value φ ∗ = (ϕ∗ , ϕ∗ , . . . , ϕn∗ , ψ∗ , ψ∗ , . . . , ψm∗ )T In addition to (H) and (H), assume that: (H) For i = , , . . . , n, j = , , . . . , m, t ∈ R, ⎧ ∞ ∞ ⎪ –ai (t)  hi (s) ds + a+i  hi (s)s ds ⎪ ⎪ ⎪ ⎨ + m [(a+ + b+ ) + (α + + β + ) +∞ k (t – s) ds]Lf < , j j ij ij ij ij j=  ∞ + ∞ ⎪ –b (t) l (s) ds + b l (s)s ds ⎪ j j  j  j ⎪ ⎪ ⎩ + n [(d+ + p+ ) + (γ + + η+ ) +∞ k (t – s) ds]Lg < . i=

ji

ji

ji

ji



i

i

(H) For i = , , . . . , n, j = , , . . . , m, k = , , . . . , Ik xi (tk ) = –θik xi (tk ), Jk yj (tk ) = –ϑjk yj (tk ),

 ≤ θik ≤ ,  ≤ ϑjk ≤ .

Then system (.) is exponentially stable. Proof Let u(t) = (x (t), x (t), . . . , xn (t), y (t), y (t), . . . , ym (t))T of equation (.) with initial value φ(s) = (ϕ (s), ϕ (s), . . . , ϕn (s), ψ (s), ψ (s), . . . , ψm (s))T . Set

x¯ i = xi (t) – x∗i (t), i = , , . . . , n, y¯ j = yj (t) – y∗j (t), j = , , . . . , m,

(.)

f¯j (¯yj (t)) = fj (¯yj (t) + y∗j (t)) – fj (y∗j (t)), j = , , . . . , m, g¯i (¯xi (t)) = gi (¯xi (t) + x∗x (t)) – gi (x∗i (t)), i = , , . . . , n.

(.)

and

For t > , t = tk , i = , , . . . , n, j = , , . . . , m, k = , , . . . , it follows from (H), (.), (.), and (.) that ⎧ ∞ ⎪ x¯ i (t) = –ai (t)  hi (s)¯xi (t – s) ds + m aij (t)f¯j (¯yj (t)) ⎪ ⎪ t m j= ⎪ m ⎪ ¯ ⎪ + j= bij (t)fj (¯yj (t – τ (t))) + j= αij (t) –∞ kj (t – s)f¯j (¯yj (s)) ds ⎪ ⎪ ⎪ t ⎨ ¯ yj (s)) ds, + m j= βij (t) –∞ kj (t – s)fj (¯ n ∞ ⎪ ⎪ y¯ j (t) = –bj (t)  lj (s)¯yj (t – s) ds + i= dji (t)¯gi (¯xi (t)) ⎪ t ⎪ ⎪ ⎪ + ni= pji (t)¯gi (¯xi (t – ρ(t))) + ni= γji (t) –∞ ki (t – s)¯gi (¯xi (s)) ds ⎪ ⎪ ⎪ t ⎩ + ni= ηji (t) –∞ ki (t – s)¯gi (¯xi (s)) ds

(.)


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and ⎧ ⎪ |xi (tk+ ) – x∗i (tk+ )| = |xi (tk ) + Ik (xi (tk )) – x∗i (tk ) – Ik (x∗i (tk ))| ⎪ ⎪ ⎪ ⎪ ⎪ = |( – θik )(xi (tk ) – x∗i (tk ))| ⎪ ⎪ ⎪ ⎨ ≤ |xi (tk ) – x∗i (tk )|, ⎪ |yj (tk+ ) – y∗j (tk+ )| = |yj (tk ) + Jk (yj (tk )) – y∗j (tk ) – Jk (y∗j (tk ))| ⎪ ⎪ ⎪ ⎪ ⎪ = |( – ϑjk )(yj (tk ) – y∗j (tk ))| ⎪ ⎪ ⎪ ⎩ ≤ |yj (tk ) – y∗j (tk )|.

(.)

By (.), we have ⎧ ⎪ |¯xi (tk+ )| = |xi (tk+ ) – x∗i (tk+ )| ⎪ ⎪ ⎪ ⎪ ⎪ ≤ |xi (tk ) – x∗i (tk )| ⎪ ⎪ ⎪ ⎨ = |¯xi (tk– )|, i = , , . . . , n, k = , , . . . , ⎪ |¯yj (tk+ )| = |yj (tk+ ) – y∗j (tk+ )| ⎪ ⎪ ⎪ ⎪ ⎪ ≤ |yi (tk ) – y∗i (tk )| ⎪ ⎪ ⎪ ⎩ = |¯yj (tk– )|, j = , , . . . , m, k = , , . . . .

(.)

Now we define continuous functions i (ς) (i = , , . . . , n) and j (ς) (j = , , . . . , m) as follows: ⎧ ∞ ∞ ⎪ i (ς) = –(ai (t)  hi (s) ds – ς) + a+i  hi (s)s ds ⎪ ⎪ +∞ ⎪ f + + ςτ+ ⎨ ) + (αij+ + βij+ )  kj (t – s)eς (s–t) ds]Lj , + m j= [(aij + bij e ∞ + ∞ ⎪ j (ς) = –(bj (t) ds ⎪  lj (s) ds – ς) + bj  lj (s)s ⎪ +∞ ⎪ + g n ⎩ + + ςρ + + + [(d + p e ) + (γ + η ) k (t – s)eς (s–t) ds]L . i=

ji

ji

ji

ji



i

(.)

i

Then we have ⎧ ∞ ∞ ⎪ i () = –ai (t)  hi (s) ds + a+i  hi (s)s ds ⎪ ⎪ +∞ ⎪ f + + + + ⎨ kj (t – s) ds]Lj < , + m j= [(aij + bij ) + (αij + βij )  ∞ ∞ ⎪ ⎪ j () = –bj (t)  lj (s) ds + b+j  lj (s)s ds ⎪ +∞ ⎪ g ⎩ + n [(d+ + p+ ) + (γ + + η+ ) k (t – s) ds]L < . i=

ji

ji

ji

ji



i

(.)

i

In view of the continuity of i (ς) (i = , , . . . , n) and j (ς) (j = , , . . . , m), then there exists a positive constant λ such that ⎧ ∞ + ∞ ⎪ ⎪ i (λ) = –(ai (t)  hi (s) ds – λ) + ai  hi(s)s ds ⎪ ⎪ +∞ f + + λτ + ⎨ ) + (αij+ + βij+ )  kj (t – s)eλ(s–t) ds]Lj < , + m j= [(aij + bij e ∞ ∞ ⎪ j (λ) = –(bj (t)  lj (s) ds – λ) + b+j  lj (s)s ds ⎪ ⎪ +∞ ⎪ + g ⎩ k (t – s)eλ(s–t) ds]L < , + n [(d+ + p+ eλρ ) + (γ + + η+ ) i=

ji

ji

ji

ji



i

(.)

i

where i = , , . . . , n, j = , , . . . , m. Let

Ui (t) = eλt x¯ i (t), i = , , . . . , n, Vj (t) = eλt y¯ j (t), j = , , . . . , m.

(.)


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It follows from (.) that d¯xi (t) dUi (t) = λeλt x¯ i (t) + eλt dt dt

∞

= λUi (t) + eλt –ai (t)

hi (s)¯xi (t – s) ds 

+

m

m ¯ aij (t)fj y¯ j (t) + bij (t)eλt f¯j y¯ j t – τ (t)

j=

+

j=

m

–∞

j=

+

kj (t – s)f¯j y¯ j (s) ds

t

αij (t)

m


t

βij (t)

–∞

j=

∞

= λUi (t) – ai (t)

hi (s) dsUi (t) + ai (t)

m



j=

m


t

αij (t)eλt –∞

j=

+

˙ i (κ) dκ ds U

t–s

m aij (t)eλt f¯j y¯ j (t) + bij (t)eλt f¯j y¯ j t – τ (t)

j=

+

t

hi (s)



+

∞

m

kj (t – s)f¯j y¯ j (s) ds,

t

λt

βij (t)e

(.)

–∞

j=

d¯yj (t) dVj (t) = λeλt y¯ j (t) + eλt dt dt = λVj (t) + eλt –bj (t)

∞

lj (s)¯yj (t – s) ds 

+

n

n dji (t)¯gi x¯ i (t) + pji (t)eλt g¯i x¯ i t – ρ(t)

i=

+

n

i=

–∞

i=

+

n

li (t – s)¯gi x¯ i (s) ds

t

γji (t)


t

ηji (t)

–∞

i=

∞

= λVj (t) – bj (t) 

+

n

+

n i=

V˙ j (κ) dκ ds

t–s

n dji (t)eλt g¯i x¯ i (t) + pji (t)eλt g¯i x¯ i t – ρ(t) i=

t

γji (t)eλt


–∞

i=

+

t

lj (s) 

i= n

∞

lj (s) dsVj (t) + bj (t)

t

ηji (t)eλt –∞

li (t – s)¯gi x¯ i (s) ds,

(.)


Page 8 of 14

where i = , , . . . , n, j = , , . . . , m. Let

˙ i (s) , max Vj (s) , V˙ j (s) , s ∈ (–∞, ] . ϒ = max max Ui (s) , U ≤i≤n

≤j≤m

It follows, for t ∈ (–∞, ], t = tk , and i = , , . . . , n, j = , , . . . , m, that

Ui (t) ≤ ϒ,

U ˙ i (t) ≤ ϒ,

Vj (t) ≤ ϒ,

V˙ j (t) ≤ ϒ.

(.)

Next we prove, for t >  and i = , , . . . , n, j = , , . . . , m, that

Ui (t) ≤ ϒ,

U ˙ i (t) ≤ ϒ,

Vj (t) ≤ ϒ,

V˙ j (t) ≤ ϒ.

(.)

If (.) does not hold true, then there exist i ∈ {, , . . . , n}, j ∈ {, , . . . , m}, and a first time t ∗ >  such that one of the following cases (.)-(.) is satisfied:

Ui (t) < ϒ, ˙ i t ∗ ≥ , U

V˙ j (t) < ϒ for t < t ∗ ;

Ui (t) < ϒ, ˙ i t ∗ ≤ , U

V˙ j (t) < ϒ for t < t ∗ ;

Ui (t) < ϒ, V˙ j t ∗ ≥ ,

V˙ j (t) < ϒ for t < t ∗ ;

Ui (t) < ϒ, V˙ j t ∗ ≤ ,

V˙ j (t) < ϒ for t < t ∗ ;

ui (t) < ϒ, ˙ i t ∗ > , U

V˙ j (t) < ϒ for t < t ∗ ;

Ui (t) < ϒ, ˙ i t ∗ < , U

V˙ j (t) < ϒ for t < t ∗ ;

Ui (t) < ϒ, V˙ j t ∗ > ,

V˙ j (t) < ϒ for t < t ∗ ;

Ui (t) < ϒ, V˙ j t ∗ < ,

V˙ j (t) < ϒ for t < t ∗ .

Ui t ∗ = ϒ,

Vj (t) < ϒ, Ui t ∗ = –ϒ,

Vj (t) < ϒ, Vj t ∗ = ϒ,

Vj (t) < ϒ, Vj t ∗ = –ϒ,

Vj (t) < ϒ, Ui t ∗ = ϒ,

Vj (t) < ϒ, Ui t ∗ = –ϒ,

Vj (t) < ϒ, Vj t ∗ = ϒ,

Vj (t) < ϒ, Vj t ∗ = –ϒ,

Vj (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

U ˙ i (t) < ϒ,

If (.) holds, then according to (H), (.), and (.), we have

∗ ∗ ∞ dUi (t)

= λUi t – ai t hi (s) dsUi t ∗ dt t=t∗  ∞ t∗ ˙ i (κ) dκ ds + ai t ∗ hi (s) U 

+

m j=

t ∗ –s

m ∗ ∗ aij t ∗ eλt f¯j y¯ j t ∗ + bij t ∗ eλt f¯j y¯ j t ∗ – τ t ∗ j=

(.)

(.)

(.)

(.)

(.)

(.)

(.)

(.)


+

m

Page 9 of 14

∗ αij t ∗ eλt

m

∗ βij t ∗ eλt

t∗

∞

 m

∗ + hi (s) ds Ui t + ai

αij+

m

t∗

–∞

βij+

t∗ –∞

j=

∗ f

kj t ∗ – s eλ(s–t ) Lj yj (s) ds

∗ f

kj (t – s)eλ(s–t ) Lj yj (s) ds

∗ ≤ λ – ai t

∞



+

m

+ hi (s) ds ϒ + ai

m

f

a+ij Lj ϒ +

+

f

b+ij eλτ Lj ϒ +

j=

m j=

βij+

∞

hi (s)s dsϒ



j=

+

hi (s)s dsϒ 

j=

m j=

+

∞

m

+ f

f

a+ij Lj Vj t ∗ + b+ij eλτ Lj Vj t ∗ – τ t ∗

j=

+


–∞

j=

∗ ≤ λ – ai t +

kj t ∗ – s f¯j y¯ j (s) ds

–∞

j=

+

t∗

m

αij+



j=

+∞

+∞

f

kj (s)eλs Lj ϒ ds

f

kj (s)eλs Lj ϒ ds



≤ – ai t ∗

∞



hi (s) ds – λ + a+i

m + + + aij + b+ij eλτ + αij+ + βij+

∞

hi (s)s ds  +∞

λ(s–t ∗ )

kj (t – s)e



j=

f ds Lj ϒ

< ,

(.)

which is a contradiction. Thus (.) is not hold true. If (.) holds, then according to (H), (.), and (.), we have

∗ ∗ ∞ dUi (t)

= λUi t – ai t hi (s) dsUi t ∗ dt t=t∗  ∞ t∗ ∗ ˙ i (κ) dκ ds + ai t hi (s) U t ∗ –s



+

m

m ∗ ∗ aij t ∗ eλt f¯j y¯ j t ∗ + bij t ∗ eλt f¯j y¯ j t ∗ – τ t ∗

j=

+

m

j=

∗ αij t ∗ eλt

m

kj t ∗ – s f¯j y¯ j (s) ds

–∞

j=

+

t∗

∗ βij t ∗ eλt

t∗


–∞

j=

≥ λ – ai t ∗ 

∞

hi (s) ds Ui t ∗ – a+i

∞

hi (s)s dsϒ 


–

m

m

+ f

f

a+ij Lj Vj t ∗ – b+ij eλτ Lj Vj t ∗ – τ t ∗

j=

–

m

j=

αij+

m

t∗ –∞

j=

–

βij+

∗ f

kj t ∗ – s eλ(s–t ) Lj yj (s) ds

t∗

∗ f

kj (t – s)eλ(s–t ) Lj yj (s) ds

–∞

j=

≥ λ – ai t ∗

∞



–

m

f

a+ij Lj ϒ –

j=

–

m j=

Page 10 of 14

hi (s) ds ϒ – a+i

m

+

f

b+ij eλτ Lj ϒ –

+∞

hi (s)s dsϒ 

m

j=

βij+

∞

αij+

j=



+∞

f

kj (s)eλs Lj ϒ ds

f

kj (s)eλs Lj ϒ ds



≥ – ai t ∗ 

∞

hi (s) ds – λ + a+i

m + + + λτ + + aij + bij e + αij + βij +

∞

hi (s)s ds 



j=

> ,

+∞

λ(s–t ∗ )

kj (t – s)e

f ds Lj (–ϒ) (.)

which is also a contradiction, thus (.) does not hold true. In a similar way, we can also prove that (.)-(.) do not hold true. On the other hand, by (.) and (.), we get ⎧ ⎪ |¯xi (tk+ )| = |xi (tk+ ) – x∗i (tk+ )| ⎪ ⎪ ⎪ ⎪ ⎪ ≤ |xi (tk ) – x∗i (tk )| = |¯xi (tk– )| ⎪ ⎪ ⎪ ⎨ = |Ui (tk– )|e–λtk ≤ |ϒe–λtk , ⎪ ⎪ |¯yj (tk+ )| = |yj (tk+ ) – y∗j (tk+ )| ⎪ ⎪ ⎪ ⎪ ≤ |yi (tk ) – y∗i (tk )| = |¯yj (tk– )| ⎪ ⎪ ⎪ ⎩ = |V (t – )|e–λtk ≤ |ϒe–λtk , j

(.)

k

where i = , , . . . , n, j = , , . . . , m, k = , , . . . . It follows from (.) and (.) that we have

|xi (t) – x∗i (t)| = O(e–λt ), |yj (t) – y∗j (t)| = O(e–λt ),

t > , i = , , . . . , n, t > , j = , , . . . , m.

(.)

Therefore system (.) is exponentially stable. The proof of Theorem . is completed. Remark . Duan and Huang [] investigated the global exponential stability of fuzzy BAM neural networks with distributed delays and time-varying delays in the leakage terms, the model (.) in [] does not involves distributed leakage delays and impulses. In this paper, we studied the exponential stability of system (.) with distributed leakage delays and impulses. System (.) is more general than those of numerous previous works. All the results obtained in [] cannot be applicable to model (.) to obtain the exponential stability of system (.). From this viewpoint, our results on the exponential stability


Page 11 of 14

for fuzzy BAM neural networks are essentially new and complement previously known results to some extent.

4 Examples In this section, we present an example to verify the analytical predictions obtained in the previous section. Consider the following fuzzy BAM cellular neural networks with distributed leakage delays and impulses: ⎧ ∞ ⎪ xi (t) = –ai (t)  hi (s)xi (t – s) ds + j= aij (t)fj (yj (t)) ⎪ ⎪ t ⎪ ⎪ ⎪ + j= bij (t)fj (yj (t – τ (t))) + j= αij (t) –∞ kj (t – s)fj (yj (s)) ds ⎪ ⎪ t ⎪ ⎪ ⎪ + j= βij (t) –∞ kj (t – s)fj (yj (s)) ds ⎪ ⎪ ⎪ ⎪ + j= Tij ωj + j= Hij ωj + j= cij (t)ωj ⎪ ⎪ ⎪ ⎪ ⎪ + Ai (t), t ≥ , t = tk , i = , , . . . , n, ⎪ ⎪ ⎪ ⎨ x (t ) = I (x (t )), i = , , . . . , n, k = , , . . . , i k k i k ∞  ⎪ (t) = –b (t) y j ⎪ j i= dji (t)gi (xi (t))  lj (s)yj (t – s) ds + ⎪ t  ⎪ ⎪ ⎪ + i= pji (t)gi (xi (t – ρ(t))) + i= γji (t) –∞ ki (t – s)gi (xi (s)) ds ⎪ ⎪ ⎪ t ⎪ ⎪ + i= ηji (t) –∞ ki (t – s)gi (xi (s)) ds ⎪ ⎪ ⎪ ⎪ ⎪ + i= Rji μi + i= Sji μi + i= qji (t)μi ⎪ ⎪ ⎪ ⎪ + Bj (t), t ≥ , t = tk , j = , , . . . , m, ⎪ ⎪ ⎪ ⎩ yj (tk ) = Jk (yj (tk )), j = , , . . . , m, k = , , . . . , where

a (t) a (t) . + .| cos t| . + .| sin t| = , . + .| sin t| . + .| sin t| b (t) b (t) . + .| sin t| . + .| cos t| a (t) a (t) = , . + .| sin t| . + .| cos t| a (t) a (t) d (t) d (t) . + . sin t . + . sin t = , . + . cos t . + . sin t d (t) d (t) . + . cos t . + . sin t b (t) b (t) = , . + . cos t . + . cos t b (t) b (t) . + . sin t . + . sin t p (t) p (t) = , . + . cos t . + . sin t p (t) p (t) α (t) α (t) . + . cos t . + . sin t = , . + . cos t . + . cos t α (t) α (t) . + . cos t . + . sin t γ (t) γ (t) , = . + . cos t . + . cos t γ (t) γ (t) . + . cos t . + . sin t β (t) β (t) = , . + . cos t . + . cos t β (t) β (t) η (t) η (t) . + . cos t . + . sin t = , . + . cos t . + . cos t η (t) η (t)

(.)


T T

T T ⎡ h (s) ⎢ ⎣ l (s) k (s) τ (t) ρ(t)

Page 12 of 14

H H R R   T = = = ,   T H H R R   A (t) A (t) . cos t . sin t T = , = ,   T . sin t . sin t B (t) B (t) ⎤ ⎡  ⎤ h (s) e–s e–s |x| |x| f (x) f (x) ⎥ ⎥ ⎢ –s –s = , l (s) ⎦ = ⎣e e ⎦, |x| |x| g (x) g (x) –s –s k (s) e e . + .| cos t|  θik  – . sin( + k) μ = , = , . + .| sin t|   + . cos( + k) μ ϑjk

where i, j = , . Then . a+ a+ = + + . b b + + . d d = + + . d d p+ p+ . = + + . p p γ+ γ+ . = + + . γ γ + + η . η = + + . η η

. , . . , . . , . . , . . , .

a– a– . . = , . . b– b– b+ b+ . . = , . . b+ b+ + + α . . α = , + + . . α α + + β . . β = , + + . . β β g g L L    = . f f   L L

It is easy to check that (H) and (H) hold. In addition, we have ∞ ∞ – + –a h (s) ds + a h (s)s ds 

+





+ aj + b+j + αj+ + βj+

j=

–a–

∞ 

+

h (s) ds + a+

+∞



j= ∞

h (s)s ds 



l (s) ds + b+

–b– +



l (s)s ds

l (s) ds + b+

f kj (t – s) ds Lj ≈ –. < ,

 +∞

g ki (t – s) ds Li ≈ –. < ,

+∞

g ki (t – s) ds Li ≈ –. < ,

∞

l (s)s ds 

 + + di + p+i + γi+ + ηi i=





i= ∞

+∞

∞

 + + + + + di + pi + γi + ηi

f kj (t – s) ds Lj ≈ –. < ,

∞

 + + + βj+ aj + b+j + αj

–b–




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which implies that (H) holds. Thus all the conditions in Theorem . are satisfied. Then we can conclude that system (.) is exponentially stable.

5 Conclusions In the present paper, we are concerned with a class of fuzzy BAM cellular neural networks with distributed leakage delays and impulses. A set of sufficient conditions to ensure the exponential stability of such fuzzy BAM cellular neural networks with distributed leakage delays and impulses are established by applying differential inequality techniques. It is shown that distributed leakage delays and impulses play an important role in exponential stability of the neural networks. The results obtained in this paper are completely new and complement the previously known work of [, ]. Competing interests The authors declare that they have no competing interests. Authors’ contributions The authors have equally made contributions. All authors read and approved the final manuscript. Author details 1 Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang, 550004, P.R. China. 2 School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471023, P.R. China. Acknowledgements The first author was supported by National Natural Science Foundation of China (No. 61673008 and No. 11261010) and Natural Science and Technology Foundation of Guizhou Province (J[2015]2025) and 125 Special Major Science and Technology of Department of Education of Guizhou Province ([2012]011). The second author was supported by National Natural Science Foundation of China (No. 11101126). The authors would like to thank the referees and the editor for helpful suggestions incorporated into this paper. Received: 9 June 2016 Accepted: 20 September 2016 References 1. Duan, LD, Huang, LH: Global exponential stability of fuzzy BAM neural networks with distributed delays and time-varying delays in the leakage terms. Neural Comput. Appl. 23, 171-178 (2013) 2. Kosko, B: Adaptive bi-directional associative memories. Appl. Opt. 26(23), 4947-4960 (1987) 3. Xu, CJ, Zhang, QM: Existence and global exponential stability of anti-periodic solutions for BAM neural networks with inertial term and delay. Neurocomputing 153, 108-116 (2015) 4. Kosko, B: Bi-directional associative memories. IEEE Trans. Syst. Man Cybern. 18(1), 49-60 (1988) 5. Xiong, WJ, Shi, YB, Cao, JD: Stability analysis of two-dimensional neutral-type Cohen-Grossberg BAM neural networks. Neural Comput. Appl. (2015). doi:10.1007/s00521-015-2099-1 6. Zhang, W, Li, CD, Huang, TW, Qi, JT: Global stability and synchronization of Markovian switching neural networks with stochastic perturbation and impulsive delay. Circuits Syst. Signal Process. 34(8), 2457-2474 (2015) 7. Wang, HM, Duan, SK, Li, CD, Wang, LD: Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects. Neural Comput. Appl. (2015). doi:10.1007/s00521-015-2094-6 8. Li, YK, Yang, L, Li, B: Existence and stability of pseudo almost periodic solution for neutral type high-order Hopfield neural networks with delays in leakage terms on time scales. Neural Process. Lett. (2015). doi:10.1007/s11063-015-9483-9 9. Cai, ZW, Huang, LH: Functional differential inclusions and dynamic behaviors for memristor-based BAM neural networks with time-varying delays. Commun. Nonlinear Sci. Numer. Simul. 19(5), 1279-1300 (2014) 10. Zhu, QX, Rakkiyappan, R, Chandrasekar, A: Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control. Neurocomputing 136, 136-151 (2014) 11. Zhang, ZQ, Liu, WB, Zhou, DM: Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays. Neural Netw. 25, 94-105 (2012) 12. Zhang, ZQ, Liu, KY: Existence and global exponential stability of a periodic solution to interval general bidirectional associative memory (BAM) neural networks with multiple delays on time scales. Neural Netw. 24(5), 427-439 (2011) 13. Li, XD: Exponential stability of Cohen-Grossberg-type BAM neural networks with time-varying delays via impulsive control. Neurocomputing 73(1-3), 525-530 (2009) 14. Anbuvithya, R, Mathiyalagan, K, Sakthivel, R, Prakash, P: Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations. Commun. Nonlinear Sci. Numer. Simul. 29(1-3), 427-440 (2015) 15. Arunkumar, A, Sakthivel, R, Mathiyalagan, K, Anthoni, SM: Robust state estimation for discrete-time BAM neural networks with time-varying delay. Neurocomputing 131, 171-178 (2014) 16. Berezansky, L, Braverman, E, Idels, L: New global exponential stability criteria for nonlinear delay differential systems with applications to BAM neural networks. Appl. Math. Comput. 243, 899-910 (2014)


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