Open Access

New Results of a Class of Two-Neuron Networks with Time-Varying Delays

Journal of Inequalities and Applications20092008:648148

DOI: 10.1155/2008/648148

Received: 19 September 2008

Accepted: 4 December 2008

Published: 5 March 2009


With the help of the continuation theorem of the coincidence degree, a priori estimates, and differential inequalities, we make a further investigation of a class of planar systems, which is generalization of some existing neural networks under a time-varying environment. Without assuming the smoothness, monotonicity, and boundedness of the activation functions, a set of sufficient conditions is given for checking the existence of periodic solution and global exponential stability for such neural networks. The obtained results extend and improve some earlier publications.

1. Introduction

Neural networks are complex and large-scale nonlinear dynamics, while the dynamics of the delayed neural network are even richer and more complicated [1]. To obtain a deep and clear understanding of the dynamics of neural networks, one of the usual ways is to investigate the delayed neural network models with two neurons, which can be described by differential systems (see [28]). It is hoped that, through discussing the dynamics of two-neuron networks, we can get some light for our understanding about the large networks. In [7], Táboas considered the system of delay differential equations
which arises as a model for a network of two saturating amplifiers (or neurons) with delayed outputs, where is a constant, and are bounded functions on satisfying
and the negative feedback conditions: , ; , . Táboas showed that there is an such that for , there exists a nonconstant periodic solution with period greater than . Further study on the global existence of periodic solutions to system (1.1) can be found in [2, 3]. All together there is only one delay appearing in both equations. Ruan and Wei [6] investigated the existence of nonconstant periodic solutions of the following planar system with two delays

where , , and are constants, the functions and satisfy , , ; for ; for ; , .

Recently, Chen and Wu [9] considered the following system:

where and are positive constants, and are nonnegative constants with , and are constants, and and are bounded functions with , and for . By discussing a two-dimensional unstable manifold of the transformation of the system (1.4), they got some results about slowly oscillating periodic solutions.

However, delays considered in all above systems (1.1)–(1.4) are constants. It is well known that the delays in artificial neural networks are usually time-varying, and they sometime vary violently with time due to the finite switching speed of amplifiers and faults in the electrical circuit. They slow down the transmission rate and tend to introduce some degree of instability in circuits. Therefore, fast response must be required in practical artificial neural-network designs. As pointed out by Gopalsamy and Sariyasa [10, 11], it would be of great interest to study the neural networks in periodic environments. On the other hand, drop the assumptions of continuous first derivative, monotonicity, and boundedness for the activation might be better candidates for some purposes, (see [12]). Motivated by above mentioned, in this paper, we continue to consider the following planar system:

where , , are periodic with a common period , , , being -periodic.

Under the help of the continuation theorem of the coincidence degree, a priori estimates, and differential inequalities, we make a further investigation of system (1.5). Without assuming the smoothness, monotonicity, and boundedness of the activation functions, a family of sufficient conditions are given for checking the existence of periodic solution and global exponential stability for such neural networks. Our results extend and improve some earlier publications. The remainder of this article is organized as follows. In Section 2, the basic notations and assumptions are introduced. After giving the criteria for checking the existence of periodic solution and global exponential stability for the neuron networks in Section 3, one illustrative example and simulations are given in Section 4. We also conclude this paper in Section 5.

2. Preliminaries

In this section, we state some notations, definitions, and lemmas. Assume that nonlinear system (1.5) is supplemented with initial values of the type
in which are continuous functions. Set , if is a solution of system (1.5), then we denote

Definition 2.1.

The solution is said to be globally exponentially stable, if there exist and such that for any solution of (1.5), one has

where is called to be globally exponentially convergent rate.

To establish the main results of the model (1.5), some of the following assumptions will be applied:

(H1) for all , where are constants;

(H2) there exist constants such that for any .

For , one denotes

To obtain the existence of the periodic solution of (1.5), we will introduce some results from Gaines and Mawhin [13].

Consider an abstract equation in a Branch space In this section, we use the coincidence degree theory to obtain the existence of an -periodic solution to (1.5). For the sake of convenience, we briefly summarize the theory as below.

Let and be normed spaces, be a linear mapping, and be a continuous mapping. The mapping will be called a Fredholm mapping of index zero if and is closed in . If is a Fredholm mapping of index zero, then there exist continuous projectors and such that and . It follows that is invertible. We denote the inverse of this map by . If is a bounded open subset of , the mapping is called -compact on if is bounded and is compact. Because is isomorphic to , there exists an isomorphism .

Let be open and bounded, and , that is, is a regular value of . Here, , the critical set of , and is the Jacobian of at . Then, the degree is defined by

with the agreement that the above sum is zero if . For more details about the degree theory, one refers to the book of Deimling [14].

Now, with the above notation, we are ready to state the continuation theorem.

Lemma 2.2 (Continuation theorem [13, page 40]).

Let be a Fredholm mapping of index zero and let be -compact on . Suppose that

(a) for each , every solution of is such that ;

(b) for each and

Then, the equation has at least one solution lying in . For more details about degree theory, we refer to the book by Deimling [14].

For the simplicity of presentation, in the remaining part of this paper, we also introduce the following notation:

3. Main results

Theorem 3.1.

Suppose holds and , , then system (1.5) has a periodic solution.


Take and denote
Equipped with the norm , is a Banach space. For any , because of the periodicity, it is easy to check that
where for any , we identify it as the constant function in X with the value vector . Define given by
Then, system (1.5) can be reduced to the operator equation . It is easy to see that
and P, Q are continuous projectors such that
It follows that L is a Fredholm mapping of index zero. Furthermore, the generalized inverse (to ) is given by
Clearly, and are continuous. For any bounded open subset , is obviously bounded. Moreover, applying the Arzela-Ascoli theorem, one can easily show that is compact. Note that is a compact operator and is bounded, therefore, is -compact on with any bounded open subset . Since , we take the isomorphism of onto to be the identity mapping. Corresponding to equation , we have
Now we reach the position to search for an appropriate open bounded subset for the application of the Lemma 2.2. Assume that is a solution of system (3.9) for some . Then, the components of are continuously differentiable. Thus, there exists such that . Hence, . This implies
we get
Set , , we find that
Now, we choose a constant number and take
where , . We will show that satisfies all the requirements given in Lemma 2.2. In fact, we will prove that if then for . Therefore, it means that is uniformly bounded with respect to when the initial value function belongs to . It follows from (3.12) and (3.13) that
. Therefore,
Clearly, , are independent of . It is easy to see that there are no and such that . If , then is a constant vector in with for . Note that , we have
We claim that
Contrarily, suppose that there exists some such that , that is,
So, we have
this is a contradiction. Therefore, (3.17) holds, and hence,
Now, consider the homotopy , defined by
where and . When and , is a constant vector in with for . Thus
We claim that
Contrarily, suppose that , then,
This is impossible. Thus, (3.22) holds. From the property of invariance under a homotopy, it follows that

We have shown that satisfies all the assumptions of Lemma 2.2. Hence, has at least one -periodic solution on . This completes the proof.

Corollary 3.2.

Suppose that there exist positive constants such that for . Then system (1.5) has at least an -periodic solution.


As ( ) implies that , hence, the conditions in Theorem 2.1 are all satisfied.

Remark 3.3.

From Corollary 3.2, we can find that the condition in [4, Theorem 2.1] can be dropped out. Therefore, Theorem 3.1 is greatly generalized results to [4, Theorem 2.1]. Furthermore, we should point out that the Theorem 3.1 is different from the the existing work in [5] when without assuming the boundedness, monotonicity, and differentiability of activation functions. In fact, the explicit presence of in Theorem 2.1 (see [5]) may impose a very strict constraint on the coefficients of (1.5) (e.g., when is very large or small). Since our results are presented independent of , it is more convenient to design a neural network with delays.

Theorem 3.4.

Suppose that satisfy the hypotheses . If , , then system (1.5) has exactly one periodic solution . Moreover, it is globally exponentially stable.


From , we can conclude is true. It is obvious that all the hypotheses in Theorem 2.1 hold with Thus, system (1.5) has at least one periodic solution, say . Let be an arbitrary solution of system (1.5). For , a direct calculation of the upper left derivative of along the solutions of system (1.5) leads to
where denotes the upper left derivative, . Let . Then, (3.27) can be transformed into
From , and (3.13), we have
Then, there exists a constant such that
Thus, we can choose a constant such that
Now, we choose a constant such that
for . From (3.32) and (3.34), we obtain
In view of (3.33) and (3.34), we have
We claim that
Contrarily, there must exist and such that
It follows that
From (3.29), (3.35), and (3.38), we obtain
which contradicts (3.39). Hence, (3.37) holds. Letting and , from (3.34) and (3.37), we have

This completes the proof.

Remark 3.5.

From Theorem 3.4, it is easy to see that our results independent of and , we have dropped out the condition (b) of Theorem 3.1 in [4] and the condition: of Theorem 3.1 in [5]. Therefore, it is generalized the corresponding results in [4, 5].

4. Example and Numerical Simulation

In this section, we will give a example to illustrate our results. Using the method of numerical simulation in [15], we will find that the theoretical conclusions are in excellent agreement with the numerically observed behavior.

Example 4.1.

Consider a two-neuron networks with delays as follows:

where and are any -periodic continuous functions for .

Obviously, the requirements of smoothness, monotonicity, and boundedness on the activation functions are relaxed in our model. By some simple calculations, we obtain , , , , , , . Therefore, , . Applying Theorem 3.4, there exists a unique -periodic solution for (4.1) and it is globally exponentially stable. These conclusions are verified by the following numerical simulations in Figures 1 and 2.
Figure 1

Numerical solution of system (4. 1), where for .

Figure 2

Phase trajectories of system (4. 1).

5. Conclusion

In this paper, we have derived sufficient algebraic conditions in terms of the parameters of the connection and activation functions for periodic solutions and global exponential stability of a two-neuron networks with time-varying delays. The obtained results extend and improve some earlier publications, which are all independent of the delays and the period ( ) and may be highly important significance in some applied fields.



This work was supported in part by the High-Tech Research and Development Program of China under Grant no. 2006AA04A104, China Postdoctoral Science Foundation under Grants no. 20070410300 and no. 200801336, the Foundation of Chinese Society for Electrical Engineering, and the Hunan Provincial Natural Science Foundation of China under Grant no. 07JJ4001.

Authors’ Affiliations

College of Mathematics and Computers, Changsha University of Science and Technology
College of Electrical and Information Engineering, Hunan University
College of Mathematics and Econometrics, Hunan University


  1. Wu J: Introduction to Neural Dynamics and Signal Transmission Delay, de Gruyter Series in Nonlinear Analysis and Applications. Volume 6. Walter de Gruyter, Berlin, Germany; 2001:x+182.View ArticleGoogle Scholar
  2. Baptistini MZ, Táboas PZ: On the existence and global bifurcation of periodic solutions to planar differential delay equations. Journal of Differential Equations 1996,127(2):391–425. 10.1006/jdeq.1996.0075MATHMathSciNetView ArticleGoogle Scholar
  3. Godoy SMS, dos Reis JG: Stability and existence of periodic solutions of a functional differential equation. Journal of Mathematical Analysis and Applications 1996,198(2):381–398. 10.1006/jmaa.1996.0089MATHMathSciNetView ArticleGoogle Scholar
  4. Huang C, Huang L: Existence and global exponential stability of periodic solution of two-neuron networks with time-varying delays. Applied Mathematics Letters 2006,19(2):126–134. 10.1016/j.aml.2005.04.001MATHMathSciNetView ArticleGoogle Scholar
  5. Huang C, Huang L, Yi T: Dynamics analysis of a class of planar systems with time-varying delays. Nonlinear Analysis: Real World Applications 2006,7(5):1233–1242. 10.1016/j.nonrwa.2005.11.006MATHMathSciNetView ArticleGoogle Scholar
  6. Ruan S, Wei J: Periodic solutions of planar systems with two delays. Proceedings of the Royal Society of Edinburgh. Section A 1999,129(5):1017–1032. 10.1017/S0308210500031061MATHMathSciNetView ArticleGoogle Scholar
  7. Táboas P: Periodic solutions of a planar delay equation. Proceedings of the Royal Society of Edinburgh. Section A 1990,116(1–2):85–101. 10.1017/S0308210500031395MATHMathSciNetView ArticleGoogle Scholar
  8. Liu K, Yang G: Strict stability criteria for impulsive functional differential systems. Journal of Inequalities and Applications 2008, 2008:-8.Google Scholar
  9. Chen Y, Wu J: Slowly oscillating periodic solutions for a delayed frustrated network of two neurons. Journal of Mathematical Analysis and Applications 2001,259(1):188–208. 10.1006/jmaa.2000.7410MATHMathSciNetView ArticleGoogle Scholar
  10. Gopalsamy K, Sariyasa S: Time delays and stimulus-dependent pattern formation in periodic environments in isolated neurons. IEEE Transactions on Neural Networks 2002,13(3):551–563. 10.1109/TNN.2002.1000124View ArticleMathSciNetMATHGoogle Scholar
  11. Gopalsamy K, Sariyasa S: Time delays and stimulus dependent pattern formation in periodic environments in isolated neurons—II. Dynamics of Continuous, Discrete & Impulsive Systems. Series B 2002,9(1):39–58.MATHMathSciNetGoogle Scholar
  12. Forti M, Tesi A: New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Transactions on Circuits and Systems. I 1995,42(7):354–366. 10.1109/81.401145MATHMathSciNetView ArticleGoogle Scholar
  13. Gaines RE, Mawhin JL: Coincidence Degree, and Nonlinear Differential Equations, Lecture Notes in Mathematics. Volume 568. Springer, Berlin, Germany; 1977:i+262.Google Scholar
  14. Deimling K: Nonlinear Functional Analysis. Springer, Berlin, Germany; 1985:xiv+450.MATHView ArticleGoogle Scholar
  15. Shampine LF, Thompson S: Solving DDEs in MATLAB. Applied Numerical Mathematics 2001,37(4):441–458. 10.1016/S0168-9274(00)00055-6MATHMathSciNetView ArticleGoogle Scholar


© Chuangxia Huang et al. 2008

This article is published under license to BioMed Central Ltd. 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.