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Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 574356, 10 pages

http://dx.doi.org/10.1155/2013/574356

## Global Exponential Stability of a Unique Almost Periodic Solution for Neutral-Type Cohen-Grossberg Neural Networks with Time Delays

^{1}School of Mathematics and Computer Science, Panzhihua University, Panzhihua, Sichuan 617000, China^{2}Department of Mathematics, ABa Teacher's College, Wenchuan, Sichuan 623002, China^{3}City College, Kunming University of Science and Technology, Kunming 650051, China

Received 11 July 2013; Accepted 23 August 2013

Academic Editor: Yong-Kui Chang

Copyright © 2013 Yongzhi Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper is concerned with the problem of the existence, uniqueness, and global exponential stability of almost periodic solution for neutral-type Cohen-Grossberg neural networks with time delays. Based on fixed point theory and Lyapunov functional, several sufficient conditions are established for the existence, uniqueness, and global exponential stability of almost periodic solution for the above system. Finally, an example and numerical simulations are given to illustrate the feasibility and effectiveness of our main results.

#### 1. Introduction

In recent years, the Cohen-Grossberg neural networks [1] have been extensively studied because of their immense potentials of application perspective in different areas such as pattern recognition, optimization, and signal and image processing. It is well known that studies on Cohen-Grossberg neural networks not only involve a discussion of stability properties, but also involve many dynamic behaviors such as periodic oscillatory behavior and almost periodic oscillatory properties. In applications, if the various constituent components of the temporally nonuniform environment is with incommensurable (nonintegral multiples) periods, then one has to consider the environment to be almost periodic since there is no a priori reason to expect the existence of periodic solutions. Therefore, if we consider the effects of the environmental factors, almost periodicity is sometimes more realistic and more general than periodicity. In addition, the (almost) periodic oscillatory behavior is of great interest; it has been found that applications in learning theory [2], which is motivated by the fact that learning usually requires repetition. And in pattern recognition or associative memory, the existence of stable almost periodic encoded patterns (including equilibria or periodic orbits) is an important feature [3, 4]. Hence, it is of prime importance to study the existence and stability of (almost) periodic solutions for Cohen-Grossberg neural networks with delays. We refer the reader to [5–18] and references therein.

On the other hand, owing to the complicated dynamic properties of the neural cells in the real world, the existing neural network models in many cases can not characterize the properties of a neural reaction process precisely. It is natural and important that systems will contain some information about the derivative of the past state to further describe and model the dynamics for such complex neural reactions. This new type of neural networks is called neutral neural networks or neural networks of neutral type. The motivation for us to study neural networks of neutral type comes from three aspects. First, based on biochemistry experiments, neural information may transfer across chemical reactivity, which results in a neutral-type process. Second, in view of electronics, it has been shown that neutral phenomena exist in large-scale integrated (LSI) circuits. Last, the key point is that cerebra can be considered as a super LSI circuit with chemical reactivity, which reasonably implies that the neutral dynamic behaviors should be included in neural dynamic systems [19]. To the best of our knowledge, the problem of global exponential stability of almost periodic solution for neutral-type Cohen-Grossberg neural networks (see [20–24]) has not been fully investigated in the literature.

In this papers we consider the following neutral-type Cohen-Grossberg neural networks with time delays: where denotes the state variable associated to the th neuron, represents an amplification function, is an appropriately behaved function; , , and denote the normal and the delayed activation function; , , and denote the connection strengths of the th neuron on the th neuron at time , respectively; and are the delays caused during the switching and transmission processes; denotes external input to the th neuron at time , .

Let and be the space of continuous functions and continuously differential functions which map into , respectively. In particular, , . For any bounded function, , , .

Throughout this paper, we set

For , we define the norm .

We list some assumptions which will be used in this paper. (), , , , , and are continuous almost periodic functions, .() and there exist positive constants and such that , for all , .() is almost periodic about the first argument, and there are positive constants and such that and , for all , .()There exist positive constants , , , and such that for all , .

The organization of this paper is as follows. In Section 2, we give some basic definitions and necessary lemmas which will be used in later sections. In Sections 3 and 4, by using a fixed point theorem and constructing suitable Lyapunov functional, we obtain some sufficient conditions ensuring the existence, uniqueness, and global exponential stability of almost periodic solution of system (1). Finally, an example and numerical simulations are given to illustrate that our results are feasible.

#### 2. Preliminaries

First of all, we shall transform system (1) and state some notations, which will be used in later sections.

From , the antiderivative of exists. We choose an antiderivative of that satisfies . Obviously, . By , we obtain that is strictly monotone increasing about . In view of derivative theorem for inverse function, the inverse function of is differential and . By , composition function is differentiable. Denote . It is easy to see that , , and . Substituting these equalities into system (1), we get which can be rewritten as where , is between and , .

By , , and the definition of , we obtain that is strictly monotone increasing about . Hence, is unique for any and continuous about ; moreover,

From the definition of , using Lagrange mean-value theorem, one gets

The existence and global exponential stability of almost periodic solution for system (1) are equivalent to the existence and global exponential stability of almost periodic solution for system (5), respectively. So, we investigate the existence and global exponential stability of almost periodic solution for system (5).

Let . The initial conditions associated with system (5) are of the form

Now, let us state the following definitions and lemmas, which will be useful in proving our main result.

*Definition 1 (see [25]). * is called almost periodic, if for any , it is possible to find a real number , for any interval with length , there exists a number in this interval such that , for all . The collection of those functions is denoted by .

*Definition 2 (see [25]). *Let and be an continuous matrix defined on . The linear system
is said to be an exponential dichotomy on if there exist constants , projection , and the fundamental matrix satisfying

Lemma 3 (see [25]). *If the linear system has an exponential dichotomy, then almost periodic system
**
has a unique almost periodic solution which can be expressed as follows:
*

Lemma 4 (see [25]). *Let be an almost periodic function and
**Then the linear system admits an exponential dichotomy, where . *

*Definition 5. *The almost periodic solution of system (5) with the initial value is said to be globally exponentially stable if there exist constants and , for any solution of system (5) with initial value such that
where

Lemma 6 (see [26, 27]). *Let be a Banach space. Assume that is an open bounded subset of and is completely continuous satisfying
**
then has a fixed point in . *

Let with the norm . Then is a Banach space with the norm .

For given positive constant , define an open bounded subset in by

By Lemmas 3 and 4, system (5) has a unique almost periodic solution which can be expressed as follows: where where , for all .

Let the map be defined by

Lemma 7. * is completely continuous. *

*Proof. *Obviously, is continuous. Next, we show that maps bounded set into itself. Assume is a positive constant and . By the almost periodicity of system (1), there exists a constant such that

Assume that , then
which implies that
where . Therefore, is a family of uniformly bounded and equicontinuous subsets. Using Arzela-Ascoli theorem, we obtain that is relatively compact. Hence, is completely continuous. The proof of this lemma is complete.

#### 3. Existence of Almost Periodic Solution

In this section, we study the existence of almost periodic solutions of system (1).

Theorem 8. *Assume that ()–() hold, suppose further that *()*, where
**then system (1) admits at least one almost periodic solution. *

*Proof. *Define
where ,

Consider the following nonlinear operator:

For , we have . So, we obtain
which yield

By Lemma 6, there exists at least one fixed point satisfying , which implies that system (5) has at least one almost periodic solution with . That is, system (1) has at least one almost periodic solution. This completes the proof.

#### 4. Global Exponential Stability of Almost Periodic Solution

Theorem 9. *Assume that ()–() hold, suppose further that *()* there exists a positive constant such that
where is defined as that in Theorem 8. **Then system (1) has a unique almost periodic solution, which is globally exponentially stable. *

*Proof. *It follows from Theorem 8 that system (5) has at least one almost periodic solution with initial value . Next we show that the almost periodic solution is globally exponentially stable.

Make a transformation for system (5): , , where is arbitrary solution of system (5) with initial value .

By , there exists a small enough positive constant such that

Define
where

In view of system (5), we have

Further, it follows from (36) that

By (36) and (37), we have

For , note that
where . Next, we claim that

Contrarily, there is such that

By (38), that we have from (33)

This is a contradiction. So, our claim is valid. Therefore,

Thus, the almost periodic solution of system (5) is globally exponentially stable. That is, the corresponding almost periodic solution of system (1) is globally exponentially stable.

Without loss of generality, we assume that and are two almost periodic solutions of system (5). Then . Similar to the above argument, (43) is valid. Hence,
which implies from that . That is, . So the almost periodic solution of system (5) is unique. This completes the proof.

#### 5. An Example and Numerical Simulations

*Example 1. *Consider the following neutral-type Cohen-Grossberg neural networks:
where , ,

Then system (45) has a unique almost periodic solution, which is globally exponentially stable.

*Proof. *Corresponding to system (1), , , , , , , and . Taking , by an easy calculation, we obtain

Further, we also have

It is easy to verify that all the conditions of Theorem 9 are satisfied and that the result follows from Theorem 9. Numerical simulations show that system (45) has a unique almost periodic solution, which is globally exponentially stable (see Figures 1, 2, and 3). This completes the proof.

In [28–30], the authors studied the existence and global exponential stability of almost periodic solution for a kind of Cohen-Grossberg neural networks without neutral delays based on the exponential dichotomy with fixed point theorem. Therefore, in some sense, we generalize the results in [28–30].

#### 6. Conclusions

Compared with periodic effects, almost periodic effects are more frequent. In recent years, the study on the almost periodic behavior of neural networks has been the object of intensive analysis by numerous authors, and some of these results can be found in the literature. However, the neural networks of neutral type have not been fully investigated in the literature. In this paper, we give some sufficient conditions to ensure the existence, uniqueness, and global exponential stability of almost periodic solution for neutral-type Cohen-Grossberg neural networks. Numerical simulation is given to illustrate the feasibility of our main result. By using the method in this paper, we could study the existence, uniqueness, and global exponential stability of almost periodic solution of other (impulsive) neural networks (on time scales). We leave these for our future work.

#### Acknowledgment

The authors would like to thank the reviewers very much for their valuable suggestions on the paper.

#### References

- M. A. Cohen and S. Grossberg, “Absolute stability of global pattern formation and parallel memory storage by competitive neural networks,”
*IEEE Transactions on Systems, Man, and Cybernetics*, vol. 13, no. 5, pp. 815–826, 1983. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Z. Zeng and J. Wang, “Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli,”
*Neural Networks*, vol. 19, no. 10, pp. 1528–1537, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus - Z. Huang, S. Mohamad, and G. Cai, “${2}^{N}$ almost periodic attractors for CNNs with variable and distributed delays,”
*Journal of the Franklin Institute*, vol. 346, no. 4, pp. 391–412, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Z. Huang, S. Mohamad, X. Wang, and C. Feng, “Convergence analysis of general neural networks under almost periodic stimuli,”
*International Journal of Circuit Theory and Applications*, vol. 37, no. 6, pp. 723–750, 2009. View at Publisher · View at Google Scholar · View at Scopus - C.-H. Li and S.-Y. Yang, “Existence and attractivity of periodic solutions to non-autonomous Cohen-Grossberg neural networks with time delays,”
*Chaos, Solitons and Fractals*, vol. 41, no. 3, pp. 1235–1244, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Y. Li, “Existence and stability of periodic solutions for Cohen-Grossberg neural networks with multiple delays,”
*Chaos, Solitons and Fractals*, vol. 20, no. 3, pp. 459–466, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - C. Wu, J. Ruan, and W. Lin, “On the existence and stability of the periodic solution in the Cohen-Grossberg neural network with time delay and high-order terms,”
*Applied Mathematics and Computation*, vol. 177, no. 1, pp. 194–210, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - B. Liu and L. Huang, “Existence and exponential stability of periodic solutions for a class of Cohen-Grossberg neural networks with time-varying delays,”
*Chaos, Solitons and Fractals*, vol. 32, no. 2, pp. 617–627, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - F. Long, Y. Wang, and S. Zhou, “Existence and exponential stability of periodic solutions for a class of Cohen-Grossberg neural networks with bounded and unbounded delays,”
*Nonlinear Analysis: Real World Applications*, vol. 8, no. 3, pp. 797–810, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - B. Li and D. Xu, “Existence and exponential stability of periodic solution for impulsive Cohen-Grossberg neural networks with time-varying delays,”
*Applied Mathematics and Computation*, vol. 219, no. 5, pp. 2506–2520, 2012. View at Publisher · View at Google Scholar - Y. Li, X. Chen, and L. Zhao, “Stability and existence of periodic solutions to delayed Cohen-Grossberg BAM neural networks with impulses on time scales,”
*Neurocomputing*, vol. 72, no. 7–9, pp. 1621–1630, 2009. View at Publisher · View at Google Scholar · View at Scopus - X. Yang, “Existence and global exponential stability of periodic solution for Cohen-Grossberg shunting inhibitory cellular neural networks with delays and impulses,”
*Neurocomputing*, vol. 72, no. 10–12, pp. 2219–2226, 2009. View at Publisher · View at Google Scholar · View at Scopus - Y. Li and X. Fan, “Existence and globally exponential stability of almost periodic solution for Cohen-Grossberg BAM neural networks with variable coefficients,”
*Applied Mathematical Modelling*, vol. 33, no. 4, pp. 2114–2120, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Y. Li, T. Zhang, and Z. Xing, “The existence of nonzero almost periodic solution for Cohen-Grossberg neural networks with continuously distributed delays and impulses,”
*Neurocomputing*, vol. 73, no. 16–18, pp. 3105–3113, 2010. View at Publisher · View at Google Scholar · View at Scopus - Q. Liu and R. Xu, “Periodic solutions of high-order Cohen-Grossberg neural networks with distributed delays,”
*Communications in Nonlinear Science and Numerical Simulation*, vol. 16, no. 7, pp. 2887–2893, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Z. Zhang, G. Peng, and D. Zhou, “Periodic solution to Cohen-Grossberg BAM neural networks with delays on time scales,”
*Journal of the Franklin Institute*, vol. 348, no. 10, pp. 2759–2781, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - H. Xiang and J. Cao, “Exponential stability of periodic solution to Cohen-Grossberg-type BAM networks with time-varying delays,”
*Neurocomputing*, vol. 72, no. 7–9, pp. 1702–1711, 2009. View at Publisher · View at Google Scholar · View at Scopus - H. Xiang, J. Wang, and J. Cao, “Almost periodic solution to Cohen-Grossberg-type BAM networks with distributed delays,”
*Neurocomputing*, vol. 72, no. 16–18, pp. 3751–3759, 2009. View at Publisher · View at Google Scholar · View at Scopus - Y. Zhang, L. Guo, and C. Feng, “Stability analysis on a neutral neural network model,” in
*Advances in Intelligent Computing*, D.-S. Huang, X.-P. Zhang, and G.-B. Huang, Eds., vol. 3644 of*Lecture Notes in Computer Science*, pp. 697–706, 2005. View at Publisher · View at Google Scholar - S. Mandal and N. C. Majee, “Existence of periodic solutions for a class of Cohen-Grossberg type neural networks with neutral delays,”
*Neurocomputing*, vol. 74, no. 6, pp. 1000–1007, 2011. View at Publisher · View at Google Scholar · View at Scopus - Z. Zhang, W. Liu, and D. Zhou, “Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays,”
*Neural Networks*, vol. 25, pp. 94–105, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus - V. Covachev, H. Akça, and M. Sarr, “Discrete-time counterparts of impulsive Cohen-Grossberg neural networks of neutral type,”
*Neural, Parallel and Scientific Computations*, vol. 19, no. 3-4, pp. 345–359, 2011. View at Zentralblatt MATH · View at MathSciNet - C. Bai, “Periodic oscillation for Cohen-Grossberg-type bidirectional associative memory neural networks with neutral time-varying delays,” in
*Proceedings of the 5th International Conference on Natural Computation (ICNC '09)*, vol. 2, pp. 18–23, Tianjin, China, August 2009. View at Publisher · View at Google Scholar · View at Scopus - C.-D. Zheng, J.-W. Li, and Z. Wang, “New stability criteria for neutral-type Cohen-Grossberg neural networks with discrete and distributed delays,”
*International Journal of Computer Mathematics*, vol. 89, no. 4, pp. 443–466, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - A. M. Fink,
*Almost Periodic Differential Equations*, vol. 377 of*Lecture Notes in Mathematics*, Springer, Berlin, Germany, 1974. View at MathSciNet - M. Altman, “A fixed point theorem in Hilbert space,”
*Bulletin of the Polish Academy of Sciences*, vol. 5, pp. 19–22, 1957. View at Zentralblatt MATH · View at MathSciNet - D. J. Guo,
*Nonlinear Functional Analysis*, Shandong Science and Technology Press, Shandong, China, 2003, (Chinese). - H. Xiang and J. Cao, “Almost periodic solution of Cohen-Grossberg neural networks with bounded and unbounded delays,”
*Nonlinear Analysis: Real World Applications*, vol. 10, no. 4, pp. 2407–2419, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - H. Zhao, L. Chen, and Z. Mao, “Existence and stability of almost periodic solution for Cohen-Grossberg neural networks with variable coefficients,”
*Nonlinear Analysis: Real World Applications*, vol. 9, no. 2, pp. 663–673, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Z. Chen, D. Zhao, and J. Ruan, “Almost periodic attractor for Cohen-Grossberg neural networks with delay,”
*Physics Letters A*, vol. 373, no. 4, pp. 434–440, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet