Further Results on Dissipativity Criterion for Markovian Jump Discrete-Time Neural Networks with Two Delay Components Via Discrete Wirtinger Inequality Approach

This paper is concerned with strict \((\mathcal {Q}, \mathcal {S}, \mathcal {R})-\gamma \) - dissipativity and passivity analysis for discrete-time Markovian jump neural networks involving both leakage and discrete delays expressed in terms of two additive time-varying delay components. The discretized Wirtinger inequality is utilized to bound the forward difference of finite-sum term in the Lyapunov functional. By constructing a suitable Lyapunov–Krasovskii functional, sufficient conditions are derived to guarantee the dissipativity and passivity criteria of the proposed neural networks. These conditions are presented in terms of linear matrix inequalities (LMIs), which can be efficiently solved via LMI MATLAB Toolbox. Finally, numerical examples are given to illustrate the effectiveness of the proposed results.     

Extended dissipativity of generalised neural networks including time delays

This article explores the extended dissipativity conditions for generalised neural networks (GNNs) including interval time-varying delays. Extended dissipativity criterions are proposed by making proper Lyapunov–Krasovskii functional. The improved reciprocally convex combination and weighted integral inequality techniques are together applied in main results to establish the new extended dissipativity conditions of delayed GNNs. Finally, the feasibility and superiority of the proposed novel approach is clearly shown by numerical examples.     

Passivity and robust synchronisation of switched interval coupled neural networks with time delay

This paper is concerned with passivity and robust synchronisation of switched coupled neural networks with uncertain parameters. First, the mathematical model of switched coupled neural networks with interval uncertain parameters is established, which consists of L modes and switches from one mode to another according to the switching rule. Second, by employing passivity theory and linear matrix inequality techniques, delay-independent and delay-dependent conditions are derived to guarantee the passivity of switched interval coupled neural networks. Moreover, based on the proposed passivity results, global synchronisation criteria can be obtained for switched coupled neural networks with or without uncertain parameters. Finally, an illustrative example is provided to demonstrate the effectiveness of the obtained results.     

Stochastic dissipativity analysis on discrete-time neural networks with time-varying delays

In this paper, the problems of global dissipativity and global exponential dissipativity are investigated for discrete-time stochastic neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing stochastic analysis technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequalities (LMIs). Furthermore, when the parameter uncertainties appear in the discrete-time stochastic neural networks with time-varying delays, the delay-dependent robust dissipativity criteria are also presented. Two examples are given to show the effectiveness and less conservatism of the proposed criteria.     

Passivity analysis for uncertain BAM neural networks with time delays and reaction–diffusions

This article deals with the problem of passivity analysis for delayed reaction–diffusion bidirectional associative memory (BAM) neural networks with weight uncertainties. By using a new integral inequality, we first present a passivity condition for the nominal networks, and then extend the result to the case with linear fractional weight uncertainties. The proposed conditions are expressed in terms of linear matrix inequalities, and thus can be checked easily. Examples are provided to demonstrate the effectiveness of the proposed results.     

Event-triggered synchronization for stochastic delayed neural networks: Passivity and passification case

This article investigates the event-triggered synchronization problem of stochastic neural networks under passivity and passification cases. For saving communication resources, an event-triggered approach is engaged in the design of synchronization for the delayed stochastic neural networks. To decrease network trouble, an event-triggered scheme is suggested between the sampler and communication network. A nonfragile event-triggered controller is intended to guarantee the finite-time stability of the subsequent closed-loop system. By applying the Lyapunov–Krasovkii functional (LKF) and the novel integral inequalities, a stability criteria for an interval-time varying delay error system ensure the designed controller can fulfill the necessities of passivity and passification performance. The desired control gain and event-triggered parameters are then found based on the linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the benefits and validity of the desired control law.     

Analysis on robust passivity of uncertain neural networks with time-varying delays via free-matrix-based integral inequality

This paper investigates the robust delay-dependent passivity problem of neural networks (NNs) with time-varying delays and parameter uncertainties. A suitable augmented Lyapunov-Krasovskii functional (LKF) with triple integral term, which takes full use of the neuron activation function conditions and the information of time-delay in integral term, is constructed. Furthermore, by utilizing integral inequality proposed recently and the combining reciprocally convex method to estimate the derivative of the LKF, some less conservative robust passivity conditions are derived in terms of LMI. The superiority of presented approaches are demonstrated via two classic numerical examples.

     

Event‐triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities

In this paper, dissipative synchronization problem for the Markovian jump neural networks with time‐varying delay and general transition probabilities is investigated. An event‐triggered communication scheme is introduced to trigger the transmission only when the variation of the sampled vector exceeds a prescribed threshold condition. The transition probabilities of the Markovian jump delayed neural networks are allowed to be known, or uncertain, or unknown. By employing delay system approach, a new model of synchronization error system is proposed. Applying the Lyapunov‐Krasovskii functional and integral inequality combining with reciprocal convex technique, a delay‐dependent criterion is developed to guarantee the stochastic stability of the errors system and achieve strict (Q,S,R)?α dissipativity. The event‐triggered parameters can be derived by solving a set of linear matrix inequalities. A numerical example is presented to illustrate the effectiveness of the proposed design method.     

时变时滞神经网络的时滞相关鲁棒稳定性和耗散性分析

研究时变时滞神经网络的鲁棒稳定性和耗散性问题.充分利用积分项的时滞信息和激励函数条件构造一个合适的增广LK泛函;利用自由矩阵积分不等式处理LK泛函的导数,得到一个低保守性的时滞相关稳定判据;将所获得的结论延伸至神经网络的耗散性分析,并推导出一个确保神经网络严格$(\mathcalX, \mathcalY,\mathcalZ)-\gamma$-耗散的充分条件.最后通过3个数值算例验证了所提出方法的可行性和优越性.     

Stochastic stability and extended dissipativity analysis for uncertain neutral systems with semi‐Markovian jumping parameters via novel free matrix–based integral inequality

This paper investigates the issues of stochastic stability and extended dissipativity analysis for uncertain neutral systems with semi‐Markovian jumping parameters. A new criterion about the stochastic stability and extended dissipativity of uncertain neutral systems with semi‐Markovian jumping parameters is obtained based on the new Lyapunov‐Krasovskii functionals together with the introduced novel free matrix–based integral inequality. The major contribution of this study is that the stochastic stability and extended dissipativity concept for uncertain neutral systems with semi‐Markovian jumping parameters can be developed to simultaneously analyze the solutions of the L₂ ? L_∞ performance, H_∞ action, passivity behavior, and dissipativity by selecting different weighting matrices. Finally, several interesting numerical examples are provided to show the effectiveness and less conservatism of the proposed method.     

Determining Dissipativity of Switched Nonlinear Systems Using Linearization

Local strict QSR‐dissipativity of a switched nonlinear system is studied using the linearization technique in this paper. We obtain local strict QSR‐dissipativity of a switched system even if each subsystem is not locally strictly QSR dissipative by designing a switching law. The derived dissipative sufficient condition is characterized by a modified Lyapunov‐Metzler inequality that can be simplified as an LMI by assuming specific forms. Two special forms of local strict QSR‐dissipativity, local input state strict passivity and local L₂‐gain, are considered. When the approximate errors of a switched affine system satisfy certain conditions, local strict passivity can be drawn from its linearization. Finally, a numerical example is given to illustrate how to apply the proposed method to achieve passivity of switched nonlinear systems.     

Global dissipativity of memristor-based complex-valued neural networks with time-varying delays

Memristor is the new model two-terminal nonlinear circuit device in electronic circuit theory. This paper deals with the problem of global dissipativity and global exponential dissipativity for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays. Sufficient global dissipativity conditions are derived from the theory of M-matrix analysis, and the globally attractive set as well as the positive invariant set is established. By constructing Lyapunov–Krasovskii functionals and using a linear matrix inequality technique, some new sufficient conditions on global dissipativity and global exponential dissipativity of MCVNNs are derived. Finally, two numerical examples are presented to demonstrate the effectiveness of our proposed theoretical results.     

Dissipativity analysis of neural networks with time-varying delays

A new definition of dissipativity for neural networks is presented in this paper. By constructing proper Lyapunov functionals and using some analytic techniques, sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the obtained results.     

A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay

This paper is concerned with delay-dependent passivity analysis for interval neural networks with time-varying delay. By decomposing the delay interval into multiple equidistant subintervals, new Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. Employing these new LKFs, a new passivity criterion is proposed in terms of linear matrix inequalities, which is dependent on the size of the time delay. Finally, some numerical examples are given to illustrate the effectiveness of the developed techniques.     

Passivity analysis of stochastic delayed neural networks with Markovian switching

In this paper, the problem of passivity analysis is investigated for a class of stochastic delayed neural networks with Markovian switching. By applying Lyapunov functional and free-weighting matrix, delay-dependent/independent passivity criteria are presented in terms of linear matrix inequalities. The results herein include existing ones for neural networks without Markovian switching as special cases. An example is given to demonstrate the effectiveness of the proposed criteria.     

Global Passivity Analysis of Interval Neural Networks with Discrete and Distributed Delays of Neutral Type

This paper is concerned with delay-dependent passivity analysis for delayed neural networks (DNNs) of neutral type. We first discuss the passivity conditions for DNNs without uncertainties and then extend this result to the case of interval uncertainties. By partitioning the delay intervals into multiple equidistant subintervals, some appropriate Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. Considering these new LKFs and using free-weighting matrix approach, several new passivity criteria are proposed in terms of linear matrix inequalities, which are dependent on the size of the time delay. Finally, five numerical examples are given to illustrate the effectiveness and less conservatism of the developed techniques.     

时滞忆阻神经网络动力学分析与控制综述

忆阻器(Memristor)是一种无源的二端电子元件,同时也是一种纳米级元件,具有低能耗、高存储、小体积和非易失性等特点.作为一种新型的存储器件,忆阻器的研制,有望使计算机实现人脑特有的信息存储与信息处理一体化的功能,打破目前冯.诺伊曼(Von Neumann)计算机架构,为下一代计算机的研制提供一种全新的架构.鉴于忆...     

A Delay-Dependent Approach to Passivity Analysis for Uncertain Neural Networks with Time-varying Delay

This paper deals with the problem of passivity analysis for neural networks with time-varying delay, which is subject to norm-bounded time-varying parameter uncertainties. The activation functions are supposed to be bounded and globally Lipschitz continuous. Delay-dependent passivity condition is proposed by using the free-weighting matrix approach. These passivity conditions are obtained in terms of linear matrix inequalities, which can be investigated easily by using standard algorithms. Two illustrative examples are provided to demonstrate the effectiveness of the proposed criteria.     

New results on passivity analysis of uncertain neural networks with time-varying delays

In this paper, the passivity problem is investigated for a class of uncertain neural networks with generalized activation functions. By employing an appropriate Lyapunov–Krasovskii functional, a new delay-dependent criterion for the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.     

Pinning exponential synchronisation and passivity of coupled delayed reaction–diffusion neural networks with and without parametric uncertainties

In this paper, we focus on the pinning exponential synchronisation and passivity of coupled reaction–diffusion neural networks (CRDNNs) with and without parametric uncertainties, respectively. On the one hand, with the help of designed nonlinear pinning controllers and Lyapunov functional method, sufficient conditions are established to let the CRDNNs with hybrid coupling and mixed time-varying delays realise exponential synchronisation and passivity. On the other hand, considering that the external perturbations may lead the reaction–diffusion neural networks (RDNNs) parameters to containing uncertainties, the robust pinning exponential synchronisation and robust pinning passivity for coupled delayed RDNNs with parametric uncertainties are investigated by designing appropriate pinning control strategies. Finally, the effectiveness of the theoretical results are substantiated by the two given numerical examples.