Research Article | Open Access
Yongkun Li, Youqin Li, "Exponential Stability of BAM Fuzzy Cellular Neural Networks with Time-Varying Delays in Leakage Terms and Impulses", Abstract and Applied Analysis, vol. 2014, Article ID 634394, 12 pages, 2014. https://doi.org/10.1155/2014/634394
Exponential Stability of BAM Fuzzy Cellular Neural Networks with Time-Varying Delays in Leakage Terms and Impulses
BAM fuzzy cellular neural networks with time-varying delays in leakage terms and impulses are considered. Some sufficient conditions for the exponential stability of the networks are established by using differential inequality techniques. The results of this paper are completely new and complementary to the previously known results. Finally, an example is given to demonstrate the effectiveness and conservativeness of our theoretical results.
The bidirectional associative memory (BAM) neural networks were first introduced by Kosko [1–3]. It is a special class of recurrent neural networks that can store bipolar rector pairs. The BAM neural networks are composed of neurons arranged in two layers, the X-layer and Y-layer. Recently, many researchers have studied the dynamics of BAM neural networks with or without delays [4–15]. However, in mathematical modeling of real world problems, uncertainty or vagueness is unavoidable. In order to take vagueness into consideration, fuzzy theory is considered as a suitable method. In [16, 17], the authors first combined those operations with cellular neural networks (FCNNs). Some results have been reported on stability and periodicity of FCNNs. More recently, state estimation problem for the fuzzy BAM neural networks has been obtained in the paper [18, 19] and passivity criteria for the fuzzy BAM neural networks have been studied in the papers [20, 21].
Very recently, a leakage delay, which is the time delay in leakage term of the systems and a considerable factor affecting dynamics for the worse in the systems, is being put to use in the problem of stability for neural networks [22, 23]. However, so far, very little attention has been paid to neural networks with time delay in the leakage (or “forgetting”) term [24–30]. Such time delays in leakage terms are difficult to handle but have great impact on the dynamical behavior of neural networks.
In , the authors studied the following BAM fuzzy cellular network with time delay in leakage terms and discrete and unbounded distributed delays: However, time-varying delays in the leakage terms inevitably occur in electronic neural networks owing to the unavoidable finite switching speed of amplifiers. It is desirable to study the fuzzy BAM neural networks with time-varying delays in leakage terms. In , by using a fixed point theorem and differential inequality techniques, the authors studied the existence and exponential stability of equilibrium point for the following BAM neural network with time-varying delays in leakage terms on time scales: where is a time scale. Though the nonimpulsive systems have been well studied in theory and in practice, the theory of impulsive differential equations is now being recognized to be richer than the corresponding theory of differential equations without impulses (see [33–35]). What is more, very few results are available on exponential stability of equilibrium point for fuzzy BAM neural networks with time-varying delays in leakage terms and impulses.
Motivated by the above discussion, in this paper, we consider the following model: where and are the states of the th neuron and the th neuron at time , and denote the activation functions of the th neuron and the th neuron at time , and denote the inputs of the th neuron and the th neuron, and denote the bias of the th neuron and the th neuron at time , and represent the rates with which the th neuron and the th neuron at time will reset their potential to the resting state in isolation when disconnected from the networks and external inputs, , , , and denote the connection weights of the feedback template at time and , denote the connection weights of the feedforward template at time , , and , denote the connection weights of the delays fuzzy feedback MIN template at time and the delays fuzzy feedback MAX template at time , , and , are the elements of the fuzzy feedforward MIN template and fuzzy feedforward MAX template, and denote the fuzzy AND and fuzzy OR operators, and denote the transmission delays at time , , are the impulses at moments , and is a strictly increasing sequence such that , , and are the feedback kernels and satisfy , , , .
The initial conditions are given by where , , .
For convenience, for a continuous function , we denote .
Throughout this paper, we make the following assumptions.(H1)The neuron activation functions and , , , are continuous on and there exist some real constants such that for all , , .(H2)The leakage delays satisfy , and , , , .
Definition 1. Let be a solution of system (3) with the initial condition and let be any solution of system (3) with the initial condition ; if these two solutions satisfy then, we say that system (3) is exponentially stable.
Our main purpose of this paper is by using differential inequality techniques to study the exponential stability of (3). The results of this paper are completely new and complementary to the previously known results and the methods used in this paper are different from those used in [31, 32].
2. Exponential Stability
In this section, we will give some sufficient conditions to guarantee the exponential stability of system (3).
Theorem 3. Suppose that and hold. Let be a solution of system (3) with the initial condition . Furthermore, assume that(H3)(H4)There exist constants , such that Then system (3) is exponentially stable.
Proof. Let , be an arbitrary solution of system (3) with the initial condition . Set
From (3) and (11), for , , , we have
According to , we get So, which implies that Define continuous functions and by setting Then, for , , we have The continuity of and implies that there exists such that
Let From (12) and (20), we obtain Similarly, We rewrite (21) and (22) as follows: We define a positive number such that It follows that We claim that If (27) is not valid, then there exist some , some , and a first such that one of the following four cases must occur:(1), , , , for ;(2), , , , for ;(3), , , , for ;(4), , , , for . If (1) holds from (19), (23), and , we have and this is a contradiction. Hence, (1) does not hold.
If (2) holds, then, from , (19), and (24), we have This is also a contradiction.
Similarly, if (3) (or (4)) holds, we can derive a contradiction. Therefore, (27) holds.
Furthermore, together with (16) and (17), we have where , , and .
From (27) and (30), we get for all , , and . Therefore, system (3) is exponentially stable. This completes the proof.
3. An Example
In this section, we present an example to illustrate the feasibility of our results obtained in previous sections.
Example 4. Consider the following fuzzy BAM neural networks with distributed delays and impulses: