Mathematical Theories and Applications for Nonlinear Control SystemsView this Special Issue
Exponential Stability of Antiperiodic Solution for BAM Neural Networks with Time-Varying Delays
In this paper, a kind of BAM neural networks with leakage delays in the negative feedback terms and time-varying delays in activation functions was considered. By constructing a suitable Lyapunov function and using inequality techniques, some sufficient conditions to ensure the existence and exponential stability of antiperiodic solutions of these neural networks were derived. These conditions extend some results recently appearing in recent papers. Lastly, an example is given to show the feasibility of these conditions.
It is known that neural networks have been applied in numerous fields, such as pattern recognition, classification, associative memory, optimization, signal and image processing, parallel computation, and nonlinear optimization problems. Up to now, there are many works focusing on the dynamical nature of various kinds of neural networks, such as stability, periodic solution, almost periodic solution, bifurcation, and chaos (see [1–11]). However, since significant time delays are ubiquitous, it is necessary to introduce delays into communication channels which leads to delayed neural networks models. Kosko  proposed a new kind of neural networks named bidirectional associative memory (BAM) neural networks with time delays which was given by the following:where is the number of neurons in layers. In model (1), and stand for the activations of th neurons; positive constants and represent the rate with which the th neuron will reset its potential to the resting state in isolation when they are disconnected from the network and the external inputs at time ; time delays and are nonnegative functions; and and denote the components of external input source introduced from outside the network. The authors in this paper applied this model to image processing.
There are many papers about BAM neural networks and stochastic BAM neural networks; for details, see [13–18]. It is known that the existence-uniqueness and stability is an important theoretical problem in the field of dynamics systems and one can find this topic in these papers [19–22]. Always, leakage delays appear in the negative feedback term of neural networks. Based on this, Gopalsmay  proposed the following BAM neural networks and studied the stability of the equilibrium and periodic solutionsIn this system, time delays and are positive constants, where . By constructing Lyapunov-Kravsovskii functionals, using inequalities and -matrices technology, the author gave two sets of delay dependent sufficient conditions on the existence of a unique equilibrium as well as its asymptotic and exponential stability to system (2). Because of time-varying delays in the real world, Liu  proposed the following BAM neural networks and discussed the global exponential stability of the network:By constructing a Lyapunov functional, some sufficient conditions on the global exponential stability of the equilibrium for system (3) were established. There are few papers considering the variable external input. It is significative to consider time-varying delays and external input in neural networks.
Recently, Li et al.  considered the following BAM neural networks with time-varying external input:To the best of our knowledge, there are few papers focusing on the existence and stability of antiperiodic solution to BAM neural networks with time-varying delays in the leakage terms in the negative feedback term. Motivated by the above discussions, in this paper, we propose a kind of BAM neural networks with time-varying delays and external input as follows:We can easily find that model (5) extends the above models. In model (5), we consider the time-varying leakage delays in negative feedback terms and time-varying delays in activation functions. And the activation functions in model (5) are more general. If the parameters are all equal to 0, then model (5) will deduce to special model in (4). And if are all constants, then model (5) will extend the models in (2) and (3). Therefore, it is important to investigate the stability of model (5). In this paper, by constructing a suitable Lyapunov functional, we give some sufficient conditions to ensure the existence and global exponential stability of antiperiodic solutions of system (5).
The rest of this paper is organized as follows. In Section 2, we introduce some notations and give some lemmas. In Section 3, we obtain some sufficient conditions on the existence and global exponential stability of antiperiodic solution of system (5). In Section 4, we give some examples to illustrate the efficiency. In Section 5, some conclusions are given.
2. Notations and Preliminary Results
First, we give some notations. We denote For vector and matrix , we define the following norms: respectively. For where , we denote The initial conditions of the system (5) are given by
Throughout this paper, we assume that the following conditions hold.
(H1) For , there exist positive constants and such that for all .
(H2) For all , where and is a positive constant.
Lemma 2 (see ). Letand then
Lemma 3. Suppose that (H3)where are any constants. Then there exists such that
Proof. LetClearly, are continuously differential functions and satisfy that According to the intermediate value theorem, it is clear that there exist constants such that Let ; then it follows that and The proof of Lemma 3 is complete.
Lemma 4. Suppose that (H1) holds true. Then, for any solution of system (5), there exists a constant such that
Proof. From system (5), we conclude that Let where and then system (5) has the following form: By (27), we get In view of Lemma 2, we haveLet and it follows from (29) that . That is, and This completes the proof of Lemma 4.
3. Main Results
In this section, we give our main results for system (5).
Theorem 5. Suppose that (H1)-(H3) are satisfied. Then any solution of system (5) is globally exponentially stable.
Proof. First, we denote By system (5), we have which leads to Then With the same method, we havewhere Now we define a Lyapunov function as follows: where is defined by Lemma 3. Differentiating along solutions to system (5), together with (33) and (34), we have In view of Lemma 3, we have . That is to say, for all . Thus