Mathematical Problems in Engineering

Volume 2017, Article ID 6987436, 22 pages

https://doi.org/10.1155/2017/6987436

## Adaptive Exponential Synchronization for Stochastic Competitive Neural Networks with Time-Varying Leakage Delays and Reaction-Diffusion Terms

^{1}Institute of Applied Mathematics, Hebei Academy of Sciences, No. 46 South Youyi Street, Shijiazhuang 050081, China^{2}Hebei Authentication Technology Engineering Research Center, No. 46 South Youyi Street, Shijiazhuang 050081, China^{3}Institute of Applied Mathematics, Shijiazhuang Mechanical Engineering College, No. 97 West Heping Road, Shijiazhuang 050003, China

Correspondence should be addressed to Zhiqiang Wang; gro.htam-beh@gnaiqihzgnaw

Received 20 February 2017; Accepted 2 May 2017; Published 20 August 2017

Academic Editor: Marco Mussetta

Copyright © 2017 Zhiqiang Wang 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

We study the exponential synchronization problem for a class of stochastic competitive neural networks with different timescales, as well as spatial diffusion, time-varying leakage delays, and discrete and distributed time-varying delays. By introducing several important inequalities and using Lyapunov functional technique, an adaptive feedback controller is designed to realize the exponential synchronization for the proposed competitive neural networks in terms of -norm. According to the theoretical results obtained in this paper, the influences of the timescale, external stimulus constants, disposable scaling constants, and controller parameters on synchronization are analyzed. Numerical simulations are presented to show the feasibility of the theoretical results.

#### 1. Introduction

Neural networks are mathematical models that are inspired by the structure and functional aspects of biological neural networks. Meyer-Baese et al. [1] proposed competitive neural networks with different timescales, which describe the dynamics of cortical cognitive maps with unsupervised synaptic modifications. In the competitive neural networks model, there are two types of state variables: the short-term-memory (STM) variables describing the fast neural activity and the long-term-memory (LTM) variables describing the slow unsupervised synaptic modifications. Hence, there are two timescales in the competitive neural networks, one of which corresponds to the fast change of the state and the other to the slow change of the synapse by external stimuli. The above competitive neural networks are described by the following differential equations:where , is the neuron current activity level, is the synaptic efficiency, is the output of neurons, is the time constant of the neuron, denotes the connection strength of the th neuron on the th neuron, is the strength of the external stimulus, is the constant external stimulus, is the number of the constant external stimuli, and is the timescale of the STM state.

Synchronization problems of neural networks have been widely researched because of their extensive applications in secure communication, information processing, and chaos generators design. Synchronization of competitive neural networks with different timescales has attracted a great interest [2–7]. In [7], Gan et al. studied the adaptive synchronization for a class of competitive neural networks with different timescales and stochastic perturbation by constructing a Lyapunov-Krasovskii functional:where and are the discrete time-varying delay and the distributed time-varying delay, respectively; and are, respectively, the discrete time-varying delay connection strength and the distributed time-varying delay connection strength of the th neuron on the th neuron; is the disposable scaling constant.

The first term in each of the right sides of (2) is called leakage term corresponding to a stabilizing negative feedback of the system [8, 9]. In real world, the transmission delays often appear in leakage terms, which are called leakage delays [10]. It is well known that leakage delays have been incorporated into neural networks by many researchers [11–14]. However, leakage delays of neural networks in most bibliographies listed above are constants. As pointed out in [15–18], the delays in neural networks are usually time-varying. Hence, the results about the neural networks with constant delays in the leakage term are imperfect.

In addition, dynamic behaviors of neural networks derive from the interactions of neurons, which is dependent on not only the time of each neuron but also its space position [19, 20]. From this point, diffusion phenomena should not be ignored in neural networks. Many good results about reaction-diffusion neural networks have been obtained [21–25]. The boundary conditions in most literatures listed are assumed to be Dirichlet boundary conditions. In engineering applications, such as thermodynamics, Neumann boundary conditions need to be considered. As far as we know, there are few results concerning the synchronization of competitive neural networks with reaction-diffusion term under Neumann boundary conditions.

Based on the above discussion, we are concerned with the combined effects of time-varying leakage delays, stochastic perturbation, and spatial diffusion on the synchronization of competitive neural networks with Neumann boundary conditions in terms of -norm via an adaptive feedback controller to improve the previous results. To this end, we discuss the following neural networks:where and is a bound compact set with smooth boundary and in space ; with denotes the state of the th neuron at time and in space ; is the Laplace operator; and are the discrete time-varying delay and the distributed time-varying delay, respectively; is the time-varying leakage delay; corresponds to the transmission diffusion coefficient along the th neuron.

Let , where and , and then then system (3) can be rewritten aswhere . Without loss of generality, the input stimulus vector is assumed to be normalized with magnitude . System (4) is simplified to

The boundary condition of system (5) takes the formThe initial value of system (5) takes the formwhere , , , , and is the Banach space of continuous functions which maps into with the topology of uniform converge and -norm ( is a positive integer) defined by

In order to observe the exponential synchronization behavior of system (5), the response system with stochastic perturbation is designed aswhere and denote the state of the response system; and are the synchronization error system; is the noise intensity matrix and the stochastic disturbance is a Brownian motion defined on (where is the sample, is the -algebra of subsets of the sample space, and is the probability measure on ), andwhere is the mathematical expectation operator with respect to the given probability measure ; is a feedback controller of the following form:The feedback strength is updated by the following law:where and are arbitrary positive constants.

The boundary condition and initial condition for response system (9) are given in the following forms:where and .

Subtracting (5) from (9) yields the error system as follows:where .

In this paper, we give the following hypotheses.

There exists a positive constant such that the neuron activation function satisfies the following conditions: where

There exists a positive constant such that for all , and

There exist positive constants and such that or for all , .

There exist positive constants and such that or for all .

There exist positive constants and such that or for all .

The paper is organized as follows. In the next section, we introduce some definitions and state several lemmas which will be essential to our proofs. In Section 3, by constructing a suitable Lyapunov functional, some new criteria are obtained to ensure the exponential synchronization of systems (5) and (9) under the adaptive feedback controller (11) and (12). Numerical simulations are carried out in Section 4 to illustrate the feasibility of the main theoretical results. A brief conclusion is given in Section 5.

#### 2. Preliminary

In this section, we introduce some notations and lemmas which will be useful in the next section.

*Definition 1. *The noise-perturbed response system (9) and the drive system (5) can be exponentially synchronized under the adaptive controller (11) and (12) based on -norm, if there exist constants and such that where and are solutions of systems (9) and (5) with differential initial functions (14) and (7), respectively, and

Lemma 2 (Wang [26], Itô’s formula). *Let be Itô processes, and where is the space of absolutely integrable function and ( is the space of square integrable function). If , ( is the family of all nonnegative functions on which are continuously twice differentiable in and once differentiable in , then are still It processes, andwhere*

Lemma 3 (Mei et al. [27]). *Let and let . Then*

Lemma 4 (Mao [28]). *Let be continuous functions. Suppose that positive constants and satisfy Then *

Lemma 5 (Gu et al. [29]). *Suppose that is a bound domain of with a smooth boundary . are real-valued functions belonging to . Thenwhere is the gradient operator.*

Lemma 6. *Let be a positive integer and let be a bound domain of with a smooth boundary . is a real-valued function and . Thenwhere is the smallest positive eigenvalue of the Neumann boundary problem:*

The proof of Lemma 6 is attached in Appendix.

*Remark 7. *If , the integral inequality (28) is the Poincaré integral inequality in [30]. The smallest eigenvalue of the Neumann boundary problem (29) is determined by the boundary of [30]. If , then

#### 3. Exponential Synchronization Criterion

In this section, the exponential synchronization criterion of the drive system (5) and the response system (9) is obtained under the adaptive feedback controller (11) and (12). For convenience, the following denotations are introduced.

Denotewhere , , , , and , , , , , , , , , and are nonnegative real numbers, respectively.

Theorem 8. *Under assumptions , the nonlinear couple neural networks (9) and (5) can be exponentially synchronized under the adaptive feedback controller (11) and (12) based on -norm, if the following condition is also satisfied.**.*

*Proof. *Definewhere .

By (10), Itô’s differential formula, and Dini derivation, it can be deduced that From the boundary conditions (6) and (13) and Lemma 6, we getBy Lemma 4, we obtainIt follows from (23) thatIt follows from (24) thatSubstituting (34)–(37) into (33), it follows from (31) and that