Complexity

Volume 2019, Article ID 7672068, 16 pages

https://doi.org/10.1155/2019/7672068

## Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event-Triggered Strategy

Hubei Normal University, Hubei 435002, China

Correspondence should be addressed to Jin-E Zhang; moc.361@50212068gnahz

Received 13 July 2019; Accepted 18 September 2019; Published 3 November 2019

Guest Editor: Raúl Villafuerte-Segura

Copyright © 2019 Jin-E Zhang and Huan Liu. 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 proposes the event-triggered strategy (ETS) for multiple neural networks (NNs) with parameter uncertainty and time delay. By establishing event-triggered mechanism and using matrix inequality techniques, several sufficient criteria are obtained to ensure global robust exponential synchronization of coupling NNs. In particular, the coupling matrix need not be the Laplace matrix in this paper. In addition, the lower bounds of sampling time intervals are also found by the established event-triggered mechanism. Eventually, three numerical examples are offered to illustrate the obtained results.

#### 1. Introduction

Multisystem network is a complex dynamical network, which has received great attraction due to its many applications in various fields, such as secure communications and biological systems. The dynamical characteristics of multisystem networks have been paid more and more attention in the fields of science and engineering (see [1–4]). As we know, synchronization of a coupling system means that its all subsystems produce common behavior under different initial values. The coupling scheme describing the interaction rules between subsystems plays an important role in ensuring the synchronization. In particular, the synchronization of multisystem networks has become a hotpot of nonlinear scientific research because the synchronization of multisystem networks can better describe many observed natural phenomena and can create ordered multisystem networks. As a kind of control, coupling is the key to ensure the synchronization of complex dynamical networks; hence, several coupling schemes are proposed to realize the synchronization. However, most of the existing works require that the coupling matrix is always the Laplace matrix; that is, the row sums of the coupling matrix all are zero and the nondiagonal elements are nonnegative, which greatly restrict us to design a good performance coupling controller. In fact, we notice that there are some non-Laplacian coupling in complex dynamical networks, so we can try to achieve the synchronization of multisystem networks by constructing non-Laplacian coupling matrix. In addition, in recent decades, NNs have become a hot research topic because of its rich content and wide application; therefore, there are many results in the research of NNs, such as in [1, 5–12]. The authors in [1] proposed a new ETS to achieve the synchronization of multiple NNs (MNNs). The dynamical characteristics of nonautonomous fractional-order delay NNs were studied in [5]. Wu and Zeng [9] derived two anti-synchronization algorithms to realize exponential anti-synchronization of memristive recurrent NNs. In [10], some results were established to ensure the Lagrange stability of NNs with memristive synapses. Wu and Zeng [11] discussed a class of memristive NNs, and several sufficient criteria were established for exponential stabilization by using matrix inequality techniques. Wu et al. [12] proposed a novel and efficacious method to study the periodic NNs, and some new results for the periodic NNs were obtained. Therefore, it is an interesting and far-reaching research topic to study the synchronization of MNNs by constructing a proper non-Laplacian coupling matrix.

Parametric uncertainty arises from a partial understanding of mathematical models, for instance, empirical quantities and constitutive laws (see [13–22]). The uncertainty of parameters must be considered in actual system because the parameters of the model in the process of industrial control are often uncertain. It is fortunate that uncertain parameters have been considered in many models in order to describe practical problems more accurately. In [13], the dynamical characteristics of stochastic nonlinear systems with parametric uncertainty were concerned. Maharajan et al. [14] investigated the problem of enhanced results on robust finite-time passivity for uncertain discrete-time Markovian jumping BAM delayed NNs. The authors in [17] provided several novel delay-dependent stability criteria to ensure robust stability of uncertain stochastic systems. Huang et al. [20] investigated robust state estimation of uncertain neural networks by designing robust state estimator and using new bounding technique. Zhu et al. [22] discussed the stability of uncertain neutral systems, and some new criteria were provided to guarantee the stability of the model. Moreover, fractional-order system is a charming research field, which describes the real world more accurately than integer-order system. Recently, Zhang [21] studied uncertain fractional-order system and its application. The uncertain parameters of the above models are required to be norm-bounded; it is a common way to deal with these uncertain parameters by using inequality techniques. With the mature of the technology, some problems in MNNs have been well solved; however, it is still a great challenge to design a feedback controller with good performance when we analyze multisystem networks. There are many papers about the topic of parametric uncertainty, but the issue about trusted control in the MNNs with uncertain parameters still has a long way to go and much further work is worth studying. In addition, time delay is often inevitable in practical systems, which may lead to instability of the system; in other words, time delay is the important reason for instability of the model. Although parameter uncertainty and time delay bring difficulties to the theoretical analysis of dynamical characteristics of system, in practical application, the analysis of model is often unreasonable without considering uncertain parameter and time delay. Hence, theoretical knowledge and practical experience urge us to study the dynamics problems of MNNs with parameter uncertainty and time delay.

Event-triggered mechanism is a very important sampling mechanism, that is, the emergence of certain events rely on the state of the system and this is also the difference between event-triggered feedback control and the traditional sampling mechanism. Because continuous-time control requires the continuous information, it is expensive and unrealistic in the real world. Unlike continuous-time control, event-triggered mechanism is a discontinuous-time control, which only requires the local communication data. Considering the sampling period, the sampling data control scheme usually adopts zero-order hold to keep the last sampling system state and control signal which is sent to the next event. In event-triggered mechanism, the sampling will not start and the controller will not be updated, unless its size reaches the specified threshold; therefore, the greatest advantage of event-triggered mechanism is to reduce communication data. Furthermore, event-triggered mechanism has been widely studied due to its effectiveness in practical systems, for example, [23–26] and references therein. Dolk and Heemels [23] investigated a networked control system subject to event-triggered control and its application to packet losses. In [24], a new decentralized event-triggered item for distributed networked systems was proposed to reduce the waste of network resources. The event-triggered rule was proposed to solve the problem of excessive use of communication resources in [25]. The authors in [26] studied neural networks by the proposed event-triggered rule, and the rule effectively solved a large number of computational problems. It is not difficult to find that event-triggered mechanism is very useful and meaningful sampling mechanism because many practical problems can be solved via ETS, but there are few research results on the combination of event-triggered mechanism and MNNs.

Based on the above discussion, the synchronization of MNNs with parameter uncertainty and time delay is studied and emphasized. By establishing the event-triggered mechanism, this paper derives several sufficient criteria to guarantee the synchronization of the systems; it shows that these results are different from the previous ones. Furthermore, the lower bounds of sampling time intervals are also obtained; that is, if the triggering time is known, we can predict the next triggering time . Roughly speaking, this paper has three highlights: (1) Most existing works on multisystem networks require that the coupling matrix is always the Laplace matrix, in which row sums of coupling matrix all are zero and nondiagonal elements are nonnegative. Because of the particularity of Laplacian matrix, the design of coupling controller is greatly limited. In fact, we have found some non-Laplacian coupling in coupled dynamic networks. In this paper, the coupling matrix need not be the Laplace matrix; that is to say, row sums of coupling matrix can be nonzero constant and nondiagonal elements are arbitrary. (2) Some criteria are proposed to ensure the synchronization of MNNs with parameter uncertainty and time delay by establishing event-triggered mechanism and using matrix inequality techniques. Compared with the most of the existing papers, the results obtained are more simple and convenient in this paper. (3) We can find the lower bounds of the sampling time intervals by the established event-triggered mechanism, and then, we can know that the Zeno behavior will not happen.

The remaining part of this paper is designed as follows. Section 2 introduces the model of MNNs with parameter uncertainty and time delay and some preliminary knowledge. Section 3 gives several sufficient criteria to achieve the global robust exponential synchronization, and the lower bounds of the sampling time intervals are also found. Section 4 provides three numerical examples to demonstrate the obtained results. In the end, Section 5 summarizes the paper’s relevant conclusion.

#### 2. Preliminaries and Problem Formulation

Throughout the paper, represents the *n*-dimensional Euclidean space. *I* stands for an identity matrix with proper dimensionality. Let be a Banach space that composed of all the continuous functions to . and stand for 2-norm and inf-norm, respectively, and let us write in terms of for the sake of simplicity. For a matrix , , and ; furthermore, stands for its transpositive, and , , , and mean that *A* are positive definite, negative definite, positive semidefinite, and negative semidefinite, respectively. is representative of diagonal matrix. *ϕ* refers to empty set. If no otherwise specified, matrices are supposed to have compatible dimensions.

Consider a group of *r* NNs with parameter uncertainty and time delay, and the model of *k*th NNs with parameter uncertainty and time delay is given as follows:where ; ; refers to the state vector; is the real diagonal positive-definite matrix standing for the neuron self-inhibitions; is the input or bias; represents the transmission delay; and are the neuron activation functions; is the weight matrix and is the delay weight matrix; , , and are the norm-bounded uncertainty terms; and denotes the control input.

The distributed event-triggered controller is given below:for and , where refers to the sampling time sequence, represents coupling gain, and stands for the coupling matrix such that

Next, we define the following distributed event-triggered function:and the sampling time sequence satisfies ETS:for and , where refers to the control parameter and with .

*Remark 1. *Let be the *s*th threshold on the *k*th neuron. It is not difficult to find that determines sampling time of the *k*th neuron, and is closely related to the control parameter and subset . In particular, if or , the *k*th neuron will have sampling time at any ; that is to say, the Zeno behavior does happen when or . It should be noted that the transmission event is triggered and the controller is updated when the measurement error exceeds the threshold in the event-triggered controller; unlike the event-triggered controller, the impulse controller samples on the determined impulse time sequence . Thus, we can know that the sampling time sequence satisfies in the event-triggered controller, and the sampling time sequence is known in the impulse controller.

Then, we can rewritte model (1) as follows:The initial conditions of model (6) are assumed to bewhere .

Let stands for the solution of model (6); in order to avoid this paper being too long, we assume that there exists a unique solution of system (6).

System (6) is called to be globally robustly exponentially synchronized, namely, each subsystem in (6) can achieve global robust exponential synchronization if there are constants and satisfyingfor any and , where *h* stands for convergence rate.

For the sake of discussion, we give two basic assumptions.

*Assumption 1. *where and () are constant matrices with proper dimensionality and are the unknown matrix with ().

*Assumption 2. *For any and , the activation functions and are all continuous in *R* and satisfywhere and represent Lipschitz constants. Let and .

*Remark 2. *Assumption 1 guarantees the boundedness of , , and . Assumption 2 ensures the speciality of the activation functions and . The Lipschitz constants and are dependent on the activation functions and rather than fixed. Lipschitz continuity is a smoother condition than uniform continuity; intuitively, Lipschitz continuity restricts the speed of function change. Furthermore, the slope of the function satisfying Lipschitz condition must be less than a real number called Lipschitz constant. In differential equation theory, Lipschitz condition is a core condition in the existence and uniqueness theorem of solutions under initial conditions.

Next, two important lemmas are introduced as follows.

Lemma 1 (see [19]). *Let F, G, and be real matrices and is a constant; if , then*

Lemma 2 (see [18]). *Let F, G, P, and be real matrices; if and , then*(1)

*For scalar and vectors and of proper dimensionality*

*(2)*

*For matrix of suitable dimensions:*

#### 3. Main Results

Let be the synchronization error and be the measurement error for . Then, by establishing event-triggered mechanism and using matrix inequality techniques, we obtain several novel conditions to achieve global robust exponential synchronization of system (6), and the lower bounds of sampling time interval are also found.

Theorem 1. *Under ETS (5), for model (6), if Assumptions 1 and 2 hold, and () are constants, , and for , then for any .*

*Proof. *From , we havethenFor each , there exists a satisfying ; according to the event-triggered function, for any , there exists a satisfyingAccording to Lemmas 1 and 2, we getCombining (15)–(22), we obtainThus,whereand such that Since , according to (24), we have From ETS (5), we getthenhence, we can know when . The proof is finished.

*Remark 3. *By Theorem 1, we can know and implying that the Zeno behavior does not happen for sampling time sequence . In addition, if and , there is no Zeno behavior for model (6) under ETS (5).

Theorem 2. *Let condition (5), Assumption 1, and Assumption 2 hold; there is satisfying and . Then, model (6) is globally robustly exponentially synchronized under ETS (5) if there exist constants and a constant satisfyingand there is and three constants , , and satisfyingwhere , , , denotes for rearrangement of with , , stands for the number of elements in , and .*

*Proof. *By Theorem 1, for any , as , then, there exists a satisfying :wheresinceLet , then By Lemma 2, we haveSubstituting (36) and (37) into (35), we getAccording to (2) and (5), we obtainFrom ETS (5), one hasthenwhereBy (41), we haveAccording to (18), one getsCombining (39), (43), and (44), we obtainFrom the definition of , one has By (31), there is a constant such thatLet , thenwhen for any .

Letthen, we have hence,which impliesfor all , where andThen, for any and , we obtain Thus, model (6) is globally robustly exponentially synchronized with convergence rate *h*. The proof is finished.

Theorem 3. *Let condition (5), Assumption 1, and Assumption 2 hold; there is satisfying and . Moreover, if there exist constants satisfyingand there is and three constants , , and satisfying*