Mathematical Problems in Engineering

Volume 2016, Article ID 1492908, 20 pages

http://dx.doi.org/10.1155/2016/1492908

## Novel Robust Exponential Stability of Markovian Jumping Impulsive Delayed Neural Networks of Neutral-Type with Stochastic Perturbation

^{1}School of Science, Sichuan University of Science & Engineering, Sichuan 643000, China^{2}Institute of Nonlinear Physical Science, Sichuan University of Science & Engineering, Sichuan 643000, China^{3}College of Mechanical Engineering, Sichuan University of Science & Engineering, Sichuan 643000, China

Received 28 January 2016; Revised 12 April 2016; Accepted 19 May 2016

Academic Editor: Olfa Boubaker

Copyright © 2016 Yang Fang 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

The robust exponential stability problem for a class of uncertain impulsive stochastic neural networks of neutral-type with Markovian parameters and mixed time-varying delays is investigated. By constructing a proper exponential-type Lyapunov-Krasovskii functional and employing Jensen integral inequality, free-weight matrix method, some novel delay-dependent stability criteria that ensure the robust exponential stability in mean square of the trivial solution of the considered networks are established in the form of linear matrix inequalities (LMIs). The proposed results do not require the derivatives of discrete and distributed time-varying delays to be 0 or smaller than 1. Moreover, the main contribution of the proposed approach compared with related methods lies in the use of three types of impulses. Finally, two numerical examples are worked out to verify the effectiveness and less conservativeness of our theoretical results over existing literature.

#### 1. Introduction

Up to now, the stability analysis of neural networks is an important research field in modern cybernetic area, since most of the successful applications of neural networks significantly depend on the stability of the equilibrium point of neural networks. Many papers related to this problem have been published in the literature; see [1] for a survey.

During implementation of artificial neural networks, time-varying delays [2–4] are unavoidable due to finite switching speeds of the amplifiers, and the neural signal propagation is often distributed in a certain time period with the presence of an amount of parallel pathways with a variety of axon sizes and lengths. Therefore, it is necessary to consider mixed time-varying delays (discrete time-varying delay and distributed time-varying delay) to design the neural networks models. There are many works focusing on the mixed time-varying delays [5–8], among which delay-dependent criteria are generally less conservative than delay-independent ones when the sizes of time-delays are small, and the maximum allowable delay bound is the main performance index of delay-dependent stability analysis [9]. In addition, as a special type of time delayed neural networks, neutral-type neural networks precisely describe that the past state of the networks will affect the current state. Therefore, the problems of stability and synchronization for such a class of neural networks have been studied in many references; see [10–22].

It is well known that the other three sources which may lead to instability and poor performances in neural networks are stochastic perturbation, impulsive perturbations, and parametric uncertainties. Most of this viewpoint is attributable to the following three reasons: A neural network can be stabilized or destabilized by certain stochastic inputs [23–26]. In the real world, many evolutionary processes are characterized by abrupt changes at time. These changes are called impulsive phenomena, which have been found in various fields, such as physics, optimal control, and biological mathematics [27]. The effects of parametric uncertainties cannot be ignored in many applications [28–30]. Hence, stochastic perturbation, impulsive perturbations, and parametric uncertainties also should be taken into consideration when dealing with the stability issue of neural networks.

On the other hand, Markovian jumping systems [31] can be seen as a special class of hybrid systems with two different states, which involve both time-evolving and event-driven mechanisms. So such systems would be used to model the abrupt phenomena such as random failures and repairs of the components, changes in the interconnections of subsystems, and sudden environment changes. Thus, many relevant analysis results for Markovian jumping neural networks with impulses have been reported; see [32–38] and the references therein.

Recently, by using the concept of the minimum impulsive interval, Bao and Cao [11], Zhang et al. [12], and Gao et al. [13] derived some sufficient conditions to ensure exponential stability in mean square for neutral-type impulsive stochastic neural networks with Markovian jumping parameters and mixed time delays. However, in [11–13], the authors ignored parametric uncertainties. And in these three papers, the derivatives of time-varying delays need to be zero or smaller than one. So far, there are few results on the study of robust exponential stability of neutral-type impulsive stochastic neural networks with Markovian jumping parameters, mixed time-varying delays, and parametric uncertainties. More importantly, the impulses can be divided into three types to discuss the following: the impulses are stabilizing; the impulses are neutral-type (i.e., they are neither helpful for stability of neural networks nor destabilizing); and the impulses are destabilizing. Some interesting results for analyzing and synthesizing impulsive nonlinear systems that divide impulses into three types can be seen in [39–46]. In [39–41, 43], the authors studied the stability problem of impulsive neural networks with discrete time-varying delay by using the Lyapunov-Razumikhin method; several criteria for global exponential stability of the discrete-time or continuous-time neural networks are established in terms of matrix inequalities. In [42, 44–46], combining the impulsive comparison theory and triangle inequality, some important results about three-type impulses for different neural networks have been obtained. However, distributed time-varying delay has not been taken into account in all abovementioned references; how to deal with the stability problem of Markovian jumping impulsive stochastic neural networks with mixed delays is also a meaningful direction. Motivated by above discussion, based on the concepts of three-type impulses, this paper focuses on the robust exponential stability in mean square of impulsive stochastic neural networks with Markovian jumping parameters, mixed time-varying delays, and parametric uncertainties. By constructing a proper exponential-type Lyapunov-Krasovskii functional, linear matrix inequality (LMI) technique, Jensen integral inequality and free-weight matrix method, several novel sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to guarantee the robust exponential stability in mean square of the trivial solution of the considered model. Compared with references [11–13], the constructed model renders more practical factors since the parametric uncertainties have been taken into account, and the derivatives of discrete and distributed time-varying delays need to be 0 or smaller than 1. Moreover, the main contribution of the proposed approach compared with related methods lies in the use of three types of impulses.

The organization of this paper is as follows. In Section 2, the robust exponential stability problem of impulsive stochastic neural networks with Markovian jumping parameters, mixed time-varying delays, and parametric uncertainties is described and some necessary definitions and lemmas are given. Some new robust exponential stability criteria are obtained in Section 3. In Section 4, two illustrative examples are given to show the effectiveness and less conservatism of the proposed method. Finally, conclusions are given in Section 5.

*Notation*. Let denote the set of real numbers, let denote the set of all nonnegative real numbers, let and denote the -dimensional and dimensional real spaces equipped with the Euclidean norm, and let refer to the Euclidean vector norm and the induced matrix norm. denotes the set of positive integers. For any matrix , denotes that is a symmetric and positive definite matrix. If , are symmetric matrices, then means that is a negative semidefinite matrix. and mean the transpose of and the inverse of a square matrix. denotes the identity matrix with appropriate dimensions. Let and denote the family of all continuous -valued functions on with the norm . Let be an -dimensional Brownian motion defined on a complete probability space with a natural filtration (i.e., ), which satisfies and . denote the family of all measurable bounded -valued random variables such that , where stands for the correspondent expectation operator with respect to the given probability measure . The notation always denotes the symmetric block in one symmetric matrix. Matrix dimensions, if not explicitly stated, are assumed to be compatible for operations.

#### 2. Model Description and Preliminaries

Let be a right continuous Markov chain in a complete probability space taking values in a finite state space with generator given by where and . Here is the transition rate from mode to mode while is the transition rate from mode to mode .

Consider a class of impulsive stochastic neural networks of neural-type with Markovian jumping parameters, mixed time-varying delays, and parametric uncertainties, which can be presented by the following impulsive integrodifferential equation:for , where is the state vector associated with neurons at time . In the continuous part of system (2), is a diagonal matrix with positive entries ; the matrices , , and are the connection weight matrix, the discrete time-varying delay connection weight matrix, and the distributed-delay connection weight matrix, respectively; , , and are the time-varying parametric uncertainties; is the nonlinear neuron activation function which describes the behavior in which the neurons respond to each other; is a constant external input vector; , , and are, namely, the discrete time-varying delay, distributed time-varying delay, and neutral time-varying delay, which satisfy , , , , , and ; the noise perturbation (or the diffusion coefficient) is a Borel measurable function. In the discrete part of system (2), , is the impulse at the moment of time of an operator defined as ; is the impulse gain matrix at the moment of time ; the discrete instant set satisfies , ; and are the left-hand and right-hand limits of operator at , respectively; as usual, we always assume that .

*Remark 1. *In the continuous part of system (2), the evolution of state vector is driven by the evolution of the operator . Consequently, we consider state jumping of the operator at impulsive time in the discrete part of system (2). In system of [13], has been used to build the main model, which is wrong since Brown motion is nowhere differentiable with probability 1 [47].

For convenience, we denote , ; then the matrices , , , , , , , and will be written as , , , , , , , and , respectively. Therefore, system (2) can be rewritten as follows:

The initial condition of system (3) is given in the following form: for any .

To prove our main results, the following hypotheses are needed:(*H*1)All the eigenvalues of matrix , are inside the unit circle, which guarantees the stability of difference system . (*H*2)Each neuron activation function is continuous [48], and there exist scalars and such that for any , , , where and can be positive, negative, or zero. And we set (*H*3)The noise matrix is local Lipschitz continuous and satisfies the linear growth condition as well, and . Moreover, there exist positive definite matrices , , , and such that for all , , , , , and . (*H*4)The time-varying admissible parametric uncertainties , , , , are in terms of where , , , and are known real constant matrices with appropriate dimensions and is the uncertain time-varying matrix-valued function satisfying

In this paper, we always assume that some conditions are satisfied so that system (3) has a unique equilibrium point. Let be the equilibrium point of system (3). For simplicity, we can shift the equilibrium to the origin by letting . Then system (3) can be transformed into the following one: where . The initial condition of system (10) is given in terms of

Noting that and , we know that the trivial solution of system (10) exists. Thus, the stability problem of of system (3) converts to the stability problem of the trivial solution of system (10). On the other hand, from hypothesis (*H*1), we get for any , , .

Next, let denote the state trajectory from the initial data on in . Based on above discussion, system (10) has a trivial solution corresponding to the initial condition . For simplicity, we write .

The following definition and lemmas are useful for developing our main results.

*Definition 2 (see [49]). *The trivial solution of system (10) is said to be exponentially stable in mean square if for every , there exist constants and such that the following inequality holds: where is called the exponential convergence rate.

Lemma 3 (Jensen integral inequality; see Gu [50]). *For any constant matrix , any scalars and with , and a vector function such that the integrals concerned are well defined, then the following inequality holds: *

Lemma 4 (Wang et al. [51]). *For given matrices , , and with and scalar , the following inequality holds: *

*Remark 5. *Some inequalities have been widely used to derive less conservative conditions to analyze and synthesize problems of time-delay systems, for example, Gronwall-Bellman inequality [52], Halanay inequality [53], Jensen integral inequality, Wirtinger integral [54], and reciprocally convex approach [55] in which Jensen integral inequality is the most used, and Lemma 4 also holds if .

*Remark 6. *Similar to [8], we further investigate the substantial influence of the three-type impulses for the exponential stability issue of stochastic neural networks of neutral-type with both Markovian jump parameters and mixed time delays.

#### 3. Main Results

In this section, the robust exponential stability in mean square of the trivial solution for system (10) is studied under hypotheses (*H*1) to (*H*4).

Before proceeding, by using the model transformation technique, we rewritten system (10) as where

Theorem 7. *Assume that hypotheses ( H1)–(H4) hold. For given scalars , , , , and , , , the trivial solution of system (10) is robustly exponentially stable in mean square if there exist positive scalars , (, , , , , positive definite matrices , , , , , , , positive diagonal matrices , , and any real matrices of appropriate dimensions such that where and the function , , is defined asand for , , , other elements of are all equal to 0.*

*Proof. *Let , . As discussed in [56–59], is a -valued Markov process. Construct the following stochastic Lyapunov-Krasovskii functional candidate for system (10): whereFor , , denote to be the weak infinitesimal operator of the random process , ; then along the trajectory of system (10) we have whereFrom hypotheses (*H*3) and (18), we haveCombining (20) and (27) together yields If , , based on (28) and Lemma 3, it is easy to derive that Note that inequality (31) still holds if and since On the other hand, by hypothesis (*H*2), one can get that there exist positive diagonal matrices , , such that the following inequalities hold Moreover, by utilizing the well-known Newton-Leibniz formulae and (16), it can be deduced that for any matrices , , with appropriate dimensions, the following equalities also holdConsidering hypothesis (*H*4), substituting (26)–(34) and into (25) yields that for , , whereCombining Lemma 4 and (35) together yields that there exist two positive scalars and such thatApplying the Schur complement equivalence [60] to (20) yields . Therefore, , which means For , , according to (19) and (23) and , we have if , then if , then So, from inequalities (38) and (40), for all , , it is true through the mathematical induction that Similarly, based on inequalities (38) and (41), for all , , , it is true through the mathematical induction that From (23), (42), and (43), the following inequalities are, namely, hold