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# Existence and Stability of Antiperiodic Solution for a Class of Generalized Neural Networks with Impulses and Arbitrary Delays on Time Scales

*Journal of Inequalities and Applications*
**volumeÂ 2010**, ArticleÂ number:Â 132790 (2010)

## Abstract

By using coincidence degree theory and Lyapunov functions, we study the existence and global exponential stability of antiperiodic solutions for a class of generalized neural networks with impulses and arbitrary delays on time scales. Some completely new sufficient conditions are established. Finally, an example is given to illustrate our results. These results are of great significance in designs and applications of globally stable anti-periodic Cohen-Grossberg neural networks with delays and impulses.

## 1. Introduction

In this paper, we consider the following generalized neural networks with impulses and arbitrary delays on time scales:

where is an -periodic time scale and if , then is a subset of , ,, represent the right and left limits of in the sense of time scales, is a sequence of real numbers such that as . There exists a positive integer such that . Without loss of generality, we also assume that . For each interval of , we denote that , especially, we denote that .

System (1.1) includes many neural continuous and discrete time networks [1â€“9]. For examples, the high-order Hopfield neural networks with impulses and delays (see [8]):

the Cohen-Grossberg neural networks with bounded and unbounded delays (see [9]):

and so on.

Arising from problems in applied sciences, it is well known that anti-periodic problems of nonlinear differential equations have been extensively studied by many authors during the past twenty years; see [10â€“21] and references cited therein. For example, anti-periodic trigonometric polynomials are important in the study of interpolation problems [22, 23], and anti-periodic wavelets are discussed in [24].

Recently, several authors [25â€“30] have investigated the anti-periodic problems of neural networks without impulse by similar analytic skills. However, to the best of our knowledge, there are few papers published on the existence of anti-periodic solutions to neural networks with impulse.

The main purpose of this paper is to study the existence and global exponential stability of anti-periodic solutions of system (1.1) by using the method of coincidence degree theory and Lyapunov functions.

The initial conditions associated with system (1.1) are of the form

Throughout this paper, we assume that

() and there exist positive constants such that for all ;

(). There exist positive constants and such that

for all ;

(), for . There exist positive constants such that

for all and ;

() and there exist positive constants such that

for all

For convenience, we introduce the following notation:

where is an -periodic function.

The organization of this paper is as follows. In Section 2, we introduce some definitions and lemmas. In Section 3, by using the method of coincidence degree theory, we obtain the existence of the anti-periodic solutions of system (1.1). In Section 4, we give the criteria of global exponential stability of the anti-periodic solutions of system (1.1). In Section 5, an example is also provided to illustrate the effectiveness of the main results in Sections 3 and 4. The conclusions are drawn in Section 6.

## 2. Preliminaries

In this section, we will first recall some basic definitions and lemmas which can be found in books [31, 32].

Definition 2.1 (see [31]).

A time scale is an arbitrary nonempty closed subset of real numbers . The forward and backward jump operators , and the graininess are defined, respectively, by

Definition 2.2 (see [31]).

A function is called right-dense continuous provided it is continuous at right-dense point of and left-side limit exists (finite) at left-dense point of . The set of all right-dense continuous functions on will be denoted by . If is continuous at each right-dense and left-dense point, then is said to be a continuous function on , the set of continuous function will be denoted by .

Definition 2.3 (see [31]).

For , one defines the delta derivative of to be the number (if it exists) with the property that for a given , there exists a neighborhood of such that

for all

Definition 2.4 (see [31]).

If , then one defines the delta integral by

Definition 2.5 (see [33]).

For each , let be a neighborhood of . Then, one defines the generalized derivative (or dini derivative), to mean that, given , there exists a right neighborhood of such that

for each , , where .

In case is right-scattered and is continuous at , this reduces to

Similar to [34], we will give the definition of anti-periodic function on a time scale as following.

Definition 2.6.

Let be a periodic time scale with period . One says that the function is -anti-periodic if there exists a natural number such that , for all and is the smallest number such that .

If , one says that is -anti-periodic if is the smallest positive number such that for all .

Definition 2.7 (see [31]).

A function is called regressive if for all , where is the graininess function. If is regressive and right-dense continuous function, then the generalized exponential function is defined by

for , with the cylinder transformation

Let be two regressive functions, we define

Then the generalized exponential function has the following properties.

Assume that are two regressive functions, then

(i) and ;

(ii);

(iii);

(iv);

(v);

(vi);

(vii).

Lemma 2.9 (see [31]).

Assume that , are delta differentiable at . Then

The following lemmas can be found in [35, 36], respectively.

Lemma 2.10.

Let . If is -periodic, then

Lemma 2.11.

Let . For rd-continuous functions one has

Definition 2.12.

The anti-periodic solution of system (1.1) is said to be globally exponentially stable if there exist positive constants and , for any solution of system (1.1) with the initial value , such that

where

The following continuation theorem of coincidence degree theory is crucial in the arguments of our main results.

Lemma 2.13 (see [37]).

Let , be two Banach spaces, be open bounded and symmetric with . Suppose that is a linear Fredholm operator of index zero with and is L-compact. Further, one also assumes that

()

Then the equation has at least one solution on .

## 3. Existence of Antiperiodic Solutions

In this section, by using Lemma 2.13, we will study the existence of at least one anti-periodic solution of (1.1).

Theorem 3.1.

Assume that hold. Suppose further that

() is a nonsingular matrix, where, for

Then system (1.1) has at least one -anti-periodic solution.

Proof.

Let is a piecewise continuous map with first-class discontinuity points in , and at each discontinuity point it is continuous on the left. Take

are two Banach spaces with the norms

respectively, where , , is any norm of .

Set

where

where

It is easy to see that

Thus, dim Kerâ€‰â€‰codim Im, and is a linear Fredholm mapping of index zero.

Define the projectors and by

respectively. It is not difficult to show that and are continuous projectors such that

Further, let and the generalized inverse is given by

in which for all .

Similar to the proof of Theorem in [38], it is not difficult to show that , are relatively compact for any open bounded set . Therefore, is -compact on for any open bounded set .

Corresponding to the operator equation , we have

or

where

Set , in view of (3.13), and Lemma 2.11, we obtain that

where . Integrating (3.13) from to , we have from that

by , we obtain that

where . From Lemma 2.10, for any , we have

Dividing by on the both sides of (3.18) and (3.19), respectively, we obtain that

Let , such that , by the arbitrariness of in view of (3.15), (3.17), (3.20), we have

where . Thus, we have from (3.21) that

where . In addition, we have that

By (3.22), we obtain that,

where . That is,

Denote that,

Then (3.25) can be rewritten in the matrix form

From the conditions of Theorem 3.1, is a nonsingular matrix, therefore,

Let

Take

It is clear that satisfies all the requirements in Lemma 2.13 and conditionis satisfied. In view of all the discussions above, we conclude from Lemma 2.13that system (1.1) has at least one -anti-periodic solution. This completes the proof.

## 4. Global Exponential Stability of Antiperiodic Solution

Suppose that is an -anti-periodic solution of system (1.1). In this section, we will construct some suitable Lyapunov functions to study the global exponential stability of this anti-periodic solution.

Theorem 4.1.

Assume that hold. Suppose further that

()there exist positive constants such that

()for all , there exist positive constants such that

()there are -periodic functions such that ;

()there exists a positive constant such that

()impulsive operator satisfy

Then the -anti-periodic solution of system (1.1) is globally exponentially stable.

Proof.

According to Theorem 3.1, we know that system (1.1) has an -anti-periodic solution with initial value , suppose that is an arbitrary solution of system (1.1) with initial value . Then it follows from system (1.1) that

In view of system (4.5), for , we have

Hence, we can obtain from that

for , and we have from that

For any , we construct the Lyapunov functional

For , calculating the delta derivative of along solutions of system (4.5), we can get

By assumption , it concludes that

Also,

It follows that for all .

On the other hand, we have

where

It is obvious that

So we can finally get

Since , from Definition 2.12, the solution of system (1.1) is globally exponential stable. This completes the proof.

## 5. An Example

Example 5.1.

Consider the following impulsive generalized neural networks:

where

when , system (5.1) has at least one exponentially stable -anti-periodic solution.

Proof.

By calculation, we have , . It is obvious that , and are satisfied. Furthermore, we can easily calculate that

is a nonsingular matrix, thus is satisfied.

When . Take , we have that

Hence holds. By Theorems 3.1 and 4.1, system (5.1) has at least one exponentially stable -anti-periodic solution. This completes the proof.

## 6. Conclusions

Using the time scales calculus theory, the coincidence degree theory, and the Lyapunov functional method, we obtain sufficient conditions for the existence and global exponential stability of anti-periodic solutions for a class of generalized neural networks with impulses and arbitrary delays. This class of generalized neural networks include many continuous or discrete time neural networks such as, Hopfield type neural networks, cellular neural networks, Cohen-Grossberg neural networks, and so on. To the best of our knowledge, the known results about the existence of anti-periodic solutions for neural networks are all done by a similar analytic method, and only good for neural networks without impulse. Our results obtained in this paper are completely new even if the time scale or and are of great significance in designs and applications of globally stable anti-periodic Cohen-Grossberg neural networks with delays and impulses.

## References

Li X: Existence and global exponential stability of periodic solution for impulsive Cohen-Grossberg-type BAM neural networks with continuously distributed delays.

*Applied Mathematics and Computation*2009, 215(1):292â€“307. 10.1016/j.amc.2009.05.005Bai C: Global exponential stability and existence of periodic solution of Cohen-Grossberg type neural networks with delays and impulses.

*Nonlinear Analysis*2008, 9(3):747â€“761. 10.1016/j.nonrwa.2006.12.007Chen Z, Zhao D, Fu X: Discrete analogue of high-order periodic Cohen-Grossberg neural networks with delay.

*Applied Mathematics and Computation*2009, 214(1):210â€“217. 10.1016/j.amc.2009.03.083Li YK: Global stability and existence of periodic solutions of discrete delayed cellular neural networks.

*Physics Letters. A*2004, 333(1â€“2):51â€“61. 10.1016/j.physleta.2004.10.022Li YK, Xing Z: Existence and global exponential stability of periodic solution of CNNs with impulses.

*Chaos, Solitons and Fractals*2007, 33(5):1686â€“1693. 10.1016/j.chaos.2006.03.041Li YK, Lu L: Global exponential stability and existence of periodic solution of Hopfield-type neural networks with impulses.

*Physics Letters. A*2004, 333(1â€“2):62â€“71. 10.1016/j.physleta.2004.09.083Zhang Z, Zhou D: Global robust exponential stability for second-order Cohen-Grossberg neural networks with multiple delays.

*Neurocomputing*2009, 73(1â€“3):213â€“218. 10.1016/j.neucom.2009.09.003Zhang J, Gui Z: Existence and stability of periodic solutions of high-order Hopfield neural networks with impulses and delays.

*Journal of Computational and Applied Mathematics*2009, 224(2):602â€“613. 10.1016/j.cam.2008.05.042Li K: Stability analysis for impulsive Cohen-Grossberg neural networks with time-varying delays and distributed delays.

*Nonlinear Analysis*2009, 10(5):2784â€“2798. 10.1016/j.nonrwa.2008.08.005Okochi H: On the existence of periodic solutions to nonlinear abstract parabolic equations.

*Journal of the Mathematical Society of Japan*1988, 40(3):541â€“553. 10.2969/jmsj/04030541Okochi H: On the existence of anti-periodic solutions to nonlinear parabolic equations in noncylindrical domains.

*Nonlinear Analysis*1990, 14(9):771â€“783. 10.1016/0362-546X(90)90105-PChen YQ: On Massera's theorem for anti-periodic solution.

*Advances in Mathematical Sciences and Applications*1999, 9(1):125â€“128.Yin Y: Monotone iterative technique and quasilinearization for some anti-periodic problems.

*Nonlinear World*1996, 3(2):253â€“266.Yin Y: Remarks on first order differential equations with anti-periodic boundary conditions.

*Nonlinear Times and Digest*1995, 2(1):83â€“94.Aftabizadeh AR, Aizicovici S, Pavel NH: On a class of second-order anti-periodic boundary value problems.

*Journal of Mathematical Analysis and Applications*1992, 171(2):301â€“320. 10.1016/0022-247X(92)90345-EAizicovici S, McKibben M, Reich S: Anti-periodic solutions to nonmonotone evolution equations with discontinuous nonlinearities.

*Nonlinear Analysis*2001, 43: 233â€“251. 10.1016/S0362-546X(99)00192-3Chen Y, Nieto JJ, O'Regan D: Anti-periodic solutions for fully nonlinear first-order differential equations.

*Mathematical and Computer Modelling*2007, 46(9â€“10):1183â€“1190. 10.1016/j.mcm.2006.12.006Chen TY, Liu WB, Zhang JJ, Zhang MY: Existence of anti-periodic solutions for LiÃ©nard equations.

*Journal of Mathematical Study*2007, 40(2):187â€“195.Liu B: Anti-periodic solutions for forced Rayleigh-type equations.

*Nonlinear Analysis*2009, 10(5):2850â€“2856. 10.1016/j.nonrwa.2008.08.011Wang W, Shen J: Existence of solutions for anti-periodic boundary value problems.

*Nonlinear Analysis*2009, 70(2):598â€“605. 10.1016/j.na.2007.12.031Li Y, Huang L: Anti-periodic solutions for a class of LiÃ©nard-type systems with continuously distributed delays.

*Nonlinear Analysis*2009, 10(4):2127â€“2132. 10.1016/j.nonrwa.2008.03.020Delvos F-J, Knoche L: Lacunary interpolation by antiperiodic trigonometric polynomials.

*BIT*1999, 39(3):439â€“450. 10.1023/A:1022314518264Du JY, Han HL, Jin GX: On trigonometric and paratrigonometric Hermite interpolation.

*Journal of Approximation Theory*2004, 131(1):74â€“99. 10.1016/j.jat.2004.09.005Chen HL: Antiperiodic wavelets.

*Journal of Computational Mathematics*1996, 14(1):32â€“39.Peng G, Huang L: Anti-periodic solutions for shunting inhibitory cellular neural networks with continuously distributed delays.

*Nonlinear Analysis*2009, 10(4):2434â€“2440. 10.1016/j.nonrwa.2008.05.001Ou C: Anti-periodic solutions for high-order Hopfield neural networks.

*Computers & Mathematics with Applications*2008, 56(7):1838â€“1844. 10.1016/j.camwa.2008.04.029Aizicovici S, McKibben M, Reich S: Anti-periodic solutions to nonmonotone evolution equations with discontinuous nonlinearities.

*Nonlinear Analysis*2001, 43: 233â€“251. 10.1016/S0362-546X(99)00192-3Shao JY: Anti-periodic solutions for shunting inhibitory cellular neural networks with time-varying delays.

*Physics Letters, Section A*2008, 372(30):5011â€“5016. 10.1016/j.physleta.2008.05.064Li YK, Yang L: Anti-periodic solutions for Cohen-Grossberg neural networks with bounded and unbounded delays.

*Communications in Nonlinear Science and Numerical Simulation*2009, 14(7):3134â€“3140. 10.1016/j.cnsns.2008.12.002Gong S: Anti-periodic solutions for a class of Cohen-Grossberg neural networks.

*Computers & Mathematics with Applications*2009, 58(2):341â€“347. 10.1016/j.camwa.2009.03.105Bohner M, Peterson A:

*Dynamic Equations on Time Scales*. BirkhÃ¤user, Boston, Mass, USA; 2001:x+358.Bohner M, Peterson A:

*Advances in Dynamic Equations on Time Scales*. BirkhÃ¤user, Boston, Mass, USA; 2003:xii+348.Lakshmikantham V, Vatsala AS: Hybrid systems on time scales.

*Journal of Computational and Applied Mathematics*2002, 141(1â€“2):227â€“235. 10.1016/S0377-0427(01)00448-4Kaufmann ER, Raffoul YN: Periodic solutions for a neutral nonlinear dynamical equation on a time scale.

*Journal of Mathematical Analysis and Applications*2006, 319(1):315â€“325. 10.1016/j.jmaa.2006.01.063Agarwal R, Bohner M, Peterson A: Inequalities on time scales: a survey.

*Mathematical Inequalities & Applications*2001, 4(4):535â€“557.Bohner M, Fan M, Zhang J: Existence of periodic solutions in predator-prey and competition dynamic systems.

*Nonlinear Analysis*2006, 7(5):1193â€“1204. 10.1016/j.nonrwa.2005.11.002Oregan D, Cho YJ, Chen YQ:

*Topological Degree Theory and Application*. Taylor & Francis, London, UK; 2006.Li YK, Chen XR, Zhao L: Stability and existence of periodic solutions to delayed Cohen-Grossberg BAM neural networks with impulses on time scales.

*Neurocomputing*2009, 72(7â€“9):1621â€“1630.

## Acknowledgment

This work is supported by the National Natural Sciences Foundation of China under Grant 10971183.

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Li, Y., Xu, E. & Zhang, T. Existence and Stability of Antiperiodic Solution for a Class of Generalized Neural Networks with Impulses and Arbitrary Delays on Time Scales.
*J Inequal Appl* **2010**, 132790 (2010). https://doi.org/10.1155/2010/132790

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DOI: https://doi.org/10.1155/2010/132790